Overview

Dataset statistics

Number of variables69
Number of observations3275
Missing cells139907
Missing cells (%)61.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory2.4 KiB

Variable types

Numeric15
Categorical54

Alerts

IPP Classification has constant value "PLS" Constant
Distance Destination (km) (remove) has constant value "7.00313428" Constant
Incident Date Time has a high cardinality: 1792 distinct values High cardinality
City has a high cardinality: 529 distinct values High cardinality
County has a high cardinality: 311 distinct values High cardinality
Subject Sub-Category has a high cardinality: 99 distinct values High cardinality
Notify hours has a high cardinality: 373 distinct values High cardinality
Search hours has a high cardinality: 562 distinct values High cardinality
Total Time Lost has a high cardinality: 623 distinct values High cardinality
TTL Hours has a high cardinality: 838 distinct values High cardinality
IPP Coord. has a high cardinality: 663 distinct values High cardinality
Temp/H has a high cardinality: 135 distinct values High cardinality
Weather has a high cardinality: 287 distinct values High cardinality
Subject Found feature has a high cardinality: 59 distinct values High cardinality
Found Secondary (remove) has a high cardinality: 84 distinct values High cardinality
Lost Strategy has a high cardinality: 107 distinct values High cardinality
Find Coord has a high cardinality: 434 distinct values High cardinality
Find Accuracy has a high cardinality: 262 distinct values High cardinality
Comments has a high cardinality: 870 distinct values High cardinality
Incident # is highly correlated with Incident Year and 1 other fieldsHigh correlation
Incident Year is highly correlated with Incident # and 2 other fieldsHigh correlation
Incident Day is highly correlated with Disperson Angle (remove)High correlation
Age is highly correlated with Disperson Angle (remove)High correlation
Weight (Kg) is highly correlated with Track Offset (m) and 1 other fieldsHigh correlation
Temp L is highly correlated with Track Offset (m) and 2 other fieldsHigh correlation
Distance IPP (km) is highly correlated with Distance IPP (miles) (remove) and 1 other fieldsHigh correlation
Distance IPP (miles) (remove) is highly correlated with Distance IPP (km) and 1 other fieldsHigh correlation
Track Offset (m) is highly correlated with Weight (Kg) and 1 other fieldsHigh correlation
Disperson Angle (remove) is highly correlated with Incident # and 6 other fieldsHigh correlation
Total Air Hours is highly correlated with Weight (Kg)High correlation
Total Personnel is highly correlated with Total Man Hours and 1 other fieldsHigh correlation
Total Man Hours is highly correlated with Total PersonnelHigh correlation
Total Cost is highly correlated with Incident Year and 2 other fieldsHigh correlation
Incident # is highly correlated with Disperson Angle (remove)High correlation
Incident Year is highly correlated with Disperson Angle (remove)High correlation
Incident Day is highly correlated with Disperson Angle (remove)High correlation
Age is highly correlated with Disperson Angle (remove)High correlation
Weight (Kg) is highly correlated with Track Offset (m) and 1 other fieldsHigh correlation
Temp L is highly correlated with Track Offset (m) and 2 other fieldsHigh correlation
Distance IPP (km) is highly correlated with Distance IPP (miles) (remove) and 1 other fieldsHigh correlation
Distance IPP (miles) (remove) is highly correlated with Distance IPP (km) and 1 other fieldsHigh correlation
Track Offset (m) is highly correlated with Weight (Kg) and 1 other fieldsHigh correlation
Disperson Angle (remove) is highly correlated with Incident # and 6 other fieldsHigh correlation
Total Air Hours is highly correlated with Weight (Kg)High correlation
Total Man Hours is highly correlated with Total CostHigh correlation
Total Cost is highly correlated with Temp L and 1 other fieldsHigh correlation
Incident # is highly correlated with Disperson Angle (remove)High correlation
Incident Year is highly correlated with Disperson Angle (remove) and 1 other fieldsHigh correlation
Incident Day is highly correlated with Disperson Angle (remove)High correlation
Age is highly correlated with Disperson Angle (remove)High correlation
Weight (Kg) is highly correlated with Track Offset (m) and 1 other fieldsHigh correlation
Temp L is highly correlated with Track Offset (m) and 2 other fieldsHigh correlation
Distance IPP (km) is highly correlated with Distance IPP (miles) (remove) and 1 other fieldsHigh correlation
Distance IPP (miles) (remove) is highly correlated with Distance IPP (km) and 1 other fieldsHigh correlation
Track Offset (m) is highly correlated with Weight (Kg) and 1 other fieldsHigh correlation
Disperson Angle (remove) is highly correlated with Incident # and 6 other fieldsHigh correlation
Total Air Hours is highly correlated with Weight (Kg)High correlation
Total Personnel is highly correlated with Total Man Hours and 1 other fieldsHigh correlation
Total Man Hours is highly correlated with Total PersonnelHigh correlation
Total Cost is highly correlated with Incident Year and 2 other fieldsHigh correlation
Incident # is highly correlated with Data Source and 20 other fieldsHigh correlation
Data Source is highly correlated with Incident # and 27 other fieldsHigh correlation
Country is highly correlated with Incident # and 23 other fieldsHigh correlation
State/Province/Region is highly correlated with Incident # and 28 other fieldsHigh correlation
Incident Type is highly correlated with Subject Sub-Category and 3 other fieldsHigh correlation
Incident Year is highly correlated with Incident # and 13 other fieldsHigh correlation
Incident Month is highly correlated with Time and 4 other fieldsHigh correlation
Incident Day is highly correlated with Time and 4 other fieldsHigh correlation
Time is highly correlated with Incident # and 29 other fieldsHigh correlation
EcoRegion Domain is highly correlated with Data Source and 8 other fieldsHigh correlation
Population Density is highly correlated with Data Source and 13 other fieldsHigh correlation
Terrain is highly correlated with Data Source and 11 other fieldsHigh correlation
Subject Category is highly correlated with State/Province/Region and 2 other fieldsHigh correlation
Subject Sub-Category is highly correlated with Incident # and 30 other fieldsHigh correlation
Subject Activity is highly correlated with Incident # and 16 other fieldsHigh correlation
Scenario is highly correlated with Incident # and 23 other fieldsHigh correlation
Group Type is highly correlated with Data Source and 6 other fieldsHigh correlation
# Lost is highly correlated with Scenario and 2 other fieldsHigh correlation
Age is highly correlated with Time and 8 other fieldsHigh correlation
Sex is highly correlated with Time and 3 other fieldsHigh correlation
Weight (Kg) is highly correlated with Incident # and 14 other fieldsHigh correlation
Height (Cm) is highly correlated with Time and 7 other fieldsHigh correlation
Physical Fitness is highly correlated with Time and 11 other fieldsHigh correlation
Mental Fitness is highly correlated with Incident # and 17 other fieldsHigh correlation
Experience is highly correlated with Data Source and 16 other fieldsHigh correlation
Clothing is highly correlated with Country and 8 other fieldsHigh correlation
Personality is highly correlated with Incident # and 18 other fieldsHigh correlation
Subject Status is highly correlated with Data Source and 11 other fieldsHigh correlation
IPP Type is highly correlated with Time and 4 other fieldsHigh correlation
IPPAccuracy (remove) is highly correlated with Incident # and 9 other fieldsHigh correlation
Temp L is highly correlated with Country and 11 other fieldsHigh correlation
Wind (kph) (remove) is highly correlated with Incident # and 24 other fieldsHigh correlation
Snow (remove) is highly correlated with Incident # and 13 other fieldsHigh correlation
Rain (remove) is highly correlated with Incident # and 11 other fieldsHigh correlation
Light (remove) is highly correlated with Incident Day and 7 other fieldsHigh correlation
Investigative Find (remove) is highly correlated with Incident # and 15 other fieldsHigh correlation
Inv Find Details (remove) is highly correlated with Data Source and 18 other fieldsHigh correlation
Suspended (remove) is highly correlated with Incident # and 17 other fieldsHigh correlation
Subject Found feature is highly correlated with Incident # and 21 other fieldsHigh correlation
Found Secondary (remove) is highly correlated with Incident # and 35 other fieldsHigh correlation
Mobility Responsiveness (remove) is highly correlated with Incident # and 28 other fieldsHigh correlation
Distance IPP (km) is highly correlated with Weight (Kg) and 4 other fieldsHigh correlation
Distance IPP (miles) (remove) is highly correlated with Subject Found feature and 2 other fieldsHigh correlation
Track Offset (m) is highly correlated with Subject Sub-CategoryHigh correlation
Mission Close (remove) is highly correlated with Suspended (remove) and 4 other fieldsHigh correlation
Rescue Method is highly correlated with Incident # and 19 other fieldsHigh correlation
Resources Used is highly correlated with Population Density and 1 other fieldsHigh correlation
Total Air Hours is highly correlated with Data Source and 13 other fieldsHigh correlation
Total Personnel is highly correlated with Found Secondary (remove) and 3 other fieldsHigh correlation
Total Man Hours is highly correlated with Subject Status and 7 other fieldsHigh correlation
Total Cost is highly correlated with Subject Status and 2 other fieldsHigh correlation
State/Province/Region has 823 (25.1%) missing values Missing
Incident Type has 1274 (38.9%) missing values Missing
Incident Date Time has 802 (24.5%) missing values Missing
Incident Year has 802 (24.5%) missing values Missing
Incident Month has 802 (24.5%) missing values Missing
Incident Day has 802 (24.5%) missing values Missing
Time has 3260 (99.5%) missing values Missing
City has 1438 (43.9%) missing values Missing
County has 477 (14.6%) missing values Missing
Population Density has 2145 (65.5%) missing values Missing
Terrain has 1662 (50.7%) missing values Missing
Subject Sub-Category has 2640 (80.6%) missing values Missing
Subject Activity has 2164 (66.1%) missing values Missing
Scenario has 1685 (51.5%) missing values Missing
Group Type has 621 (19.0%) missing values Missing
# Lost has 418 (12.8%) missing values Missing
Age has 809 (24.7%) missing values Missing
Sex has 621 (19.0%) missing values Missing
Weight (Kg) has 3204 (97.8%) missing values Missing
Height (Cm) has 3194 (97.5%) missing values Missing
Physical Fitness has 2742 (83.7%) missing values Missing
Mental Fitness has 2159 (65.9%) missing values Missing
Experience has 2944 (89.9%) missing values Missing
Clothing has 2873 (87.7%) missing values Missing
Personality has 3239 (98.9%) missing values Missing
Subject Status has 122 (3.7%) missing values Missing
Notify hours has 1117 (34.1%) missing values Missing
Search hours has 633 (19.3%) missing values Missing
Total Time Lost has 996 (30.4%) missing values Missing
IPP Type has 3049 (93.1%) missing values Missing
IPP Classification has 3260 (99.5%) missing values Missing
IPP Coord. has 2575 (78.6%) missing values Missing
IPPAccuracy (remove) has 3271 (99.9%) missing values Missing
Destination Coord. (remove) has 3273 (99.9%) missing values Missing
Temp/H has 2109 (64.4%) missing values Missing
Temp L has 3127 (95.5%) missing values Missing
Wind (kph) (remove) has 3063 (93.5%) missing values Missing
Weather has 1710 (52.2%) missing values Missing
Snow (remove) has 2955 (90.2%) missing values Missing
Rain (remove) has 2825 (86.3%) missing values Missing
Light (remove) has 3264 (99.7%) missing values Missing
Investigative Find (remove) has 3111 (95.0%) missing values Missing
Inv Find Details (remove) has 3265 (99.7%) missing values Missing
Suspended (remove) has 3174 (96.9%) missing values Missing
Subject Found feature has 1575 (48.1%) missing values Missing
Found Secondary (remove) has 3063 (93.5%) missing values Missing
Mobility Responsiveness (remove) has 3131 (95.6%) missing values Missing
Lost Strategy has 2831 (86.4%) missing values Missing
Distance IPP (km) has 883 (27.0%) missing values Missing
Distance Destination (km) (remove) has 3274 (> 99.9%) missing values Missing
Distance IPP (miles) (remove) has 1465 (44.7%) missing values Missing
Track Offset (m) has 2101 (64.2%) missing values Missing
Find Coord has 2829 (86.4%) missing values Missing
Find Accuracy has 3007 (91.8%) missing values Missing
Disperson Angle (remove) has 3273 (99.9%) missing values Missing
Mission Close (remove) has 3115 (95.1%) missing values Missing
Rescue Method has 3166 (96.7%) missing values Missing
Resources Used has 3254 (99.4%) missing values Missing
Total Air Hours has 2880 (87.9%) missing values Missing
Total Personnel has 2540 (77.6%) missing values Missing
Total Man Hours has 2271 (69.3%) missing values Missing
Total Cost has 2428 (74.1%) missing values Missing
Comments has 2321 (70.9%) missing values Missing
Total Personnel is highly skewed (γ1 = 23.12213305) Skewed
Incident # is uniformly distributed Uniform
Incident Date Time is uniformly distributed Uniform
Time is uniformly distributed Uniform
IPP Coord. is uniformly distributed Uniform
Destination Coord. (remove) is uniformly distributed Uniform
Find Coord is uniformly distributed Uniform
Find Accuracy is uniformly distributed Uniform
Disperson Angle (remove) is uniformly distributed Uniform
Incident # has unique values Unique
Distance IPP (km) has 211 (6.4%) zeros Zeros
Distance IPP (miles) (remove) has 173 (5.3%) zeros Zeros
Track Offset (m) has 437 (13.3%) zeros Zeros
Total Air Hours has 345 (10.5%) zeros Zeros
Total Cost has 211 (6.4%) zeros Zeros

Reproduction

Analysis started2021-10-12 15:57:38.559342
Analysis finished2021-10-12 15:59:21.755215
Duration1 minute and 43.2 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Incident #
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct3275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1638.06229
Minimum1
Maximum3276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:22.000273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile164.7
Q1819.5
median1638
Q32456.5
95-th percentile3112.3
Maximum3276
Range3275
Interquartile range (IQR)1637

Descriptive statistics

Standard deviation945.6566024
Coefficient of variation (CV)0.5773019794
Kurtosis-1.199635284
Mean1638.06229
Median Absolute Deviation (MAD)819
Skewness0.0003248565694
Sum5364654
Variance894266.4097
MonotonicityStrictly increasing
2021-10-12T11:59:22.428343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
21881
 
< 0.1%
21781
 
< 0.1%
21791
 
< 0.1%
21801
 
< 0.1%
21811
 
< 0.1%
21821
 
< 0.1%
21831
 
< 0.1%
21841
 
< 0.1%
21851
 
< 0.1%
Other values (3265)3265
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
32761
< 0.1%
32751
< 0.1%
32741
< 0.1%
32731
< 0.1%
32721
< 0.1%
32711
< 0.1%
32701
< 0.1%
32691
< 0.1%
32681
< 0.1%
32671
< 0.1%

Data Source
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size193.4 KiB
PLI
940 
NZ
608 
US
394 
US-OR
292 
US-VA
168 
Other values (30)
873 

Length

Max length7
Median length3
Mean length3.431450382
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPLI
2nd rowAU
3rd rowAU
4th rowAU
5th rowAU

Common Values

ValueCountFrequency (%)
PLI940
28.7%
NZ608
18.6%
US394
12.0%
US-OR292
 
8.9%
US-VA168
 
5.1%
UK111
 
3.4%
UK-MREW110
 
3.4%
CA-OPP92
 
2.8%
AU89
 
2.7%
US-PL62
 
1.9%
Other values (25)409
12.5%

Length

2021-10-12T11:59:22.783087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pli940
28.7%
nz608
18.6%
us394
12.0%
us-or292
 
8.9%
us-va168
 
5.1%
uk111
 
3.4%
uk-mrew110
 
3.4%
ca-opp92
 
2.8%
au89
 
2.7%
us-pl62
 
1.9%
Other values (24)409
12.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size188.8 KiB
US
2177 
NZ
608 
UK
221 
CA
 
159
AU
 
89
Other values (2)
 
21

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowAU
3rd rowAU
4th rowAU
5th rowAU

Common Values

ValueCountFrequency (%)
US2177
66.5%
NZ608
 
18.6%
UK221
 
6.7%
CA159
 
4.9%
AU89
 
2.7%
PL11
 
0.3%
IE10
 
0.3%

Length

2021-10-12T11:59:23.082195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:23.219307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
us2177
66.5%
nz608
 
18.6%
uk221
 
6.7%
ca159
 
4.9%
au89
 
2.7%
pl11
 
0.3%
ie10
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

State/Province/Region
Categorical

HIGH CORRELATION
MISSING

Distinct50
Distinct (%)2.0%
Missing823
Missing (%)25.1%
Memory size172.1 KiB
VA
986 
OR
301 
Perkins, Roberts, Fenny†
110 
ON
 
98
MD
 
72
Other values (45)
885 

Length

Max length24
Median length2
Mean length2.998776509
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.3%

Sample

1st rowVA
2nd rowBC
3rd rowBC
4th rowBC
5th rowBC

Common Values

ValueCountFrequency (%)
VA986
30.1%
OR301
 
9.2%
Perkins, Roberts, Fenny†110
 
3.4%
ON98
 
3.0%
MD72
 
2.2%
NC70
 
2.1%
WA65
 
2.0%
NY62
 
1.9%
CA60
 
1.8%
KY57
 
1.7%
Other values (40)571
17.4%
(Missing)823
25.1%

Length

2021-10-12T11:59:23.949405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
va1013
37.9%
or301
 
11.3%
perkins110
 
4.1%
roberts110
 
4.1%
fenny†110
 
4.1%
on98
 
3.7%
md72
 
2.7%
nc70
 
2.6%
wa65
 
2.4%
ny62
 
2.3%
Other values (36)661
24.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Incident Type
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing1274
Missing (%)38.9%
Memory size163.1 KiB
Search
1984 
Recovery
 
9
Rescue
 
8

Length

Max length8
Median length6
Mean length6.008995502
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRecovery
2nd rowSearch
3rd rowSearch
4th rowSearch
5th rowSearch

Common Values

ValueCountFrequency (%)
Search1984
60.6%
Recovery9
 
0.3%
Rescue8
 
0.2%
(Missing)1274
38.9%

Length

2021-10-12T11:59:24.241011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:24.477171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
search1984
99.2%
recovery9
 
0.4%
rescue8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Incident Date Time
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1792
Distinct (%)72.5%
Missing802
Missing (%)24.5%
Memory size184.4 KiB
7/5/1905
 
27
7/28/2004
 
5
11/11/2004
 
5
6/2/2006
 
5
7/4/1905
 
5
Other values (1787)
2426 

Length

Max length10
Median length9
Mean length8.921957137
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1277 ?
Unique (%)51.6%

Sample

1st row7/28/2004
2nd row9/30/2000
3rd row1/7/2000
4th row11/15/2001
5th row10/9/2000

Common Values

ValueCountFrequency (%)
7/5/190527
 
0.8%
7/28/20045
 
0.2%
11/11/20045
 
0.2%
6/2/20065
 
0.2%
7/4/19055
 
0.2%
5/17/20055
 
0.2%
3/21/20055
 
0.2%
7/19/20045
 
0.2%
6/10/20045
 
0.2%
4/17/20045
 
0.2%
Other values (1782)2401
73.3%
(Missing)802
 
24.5%

Length

2021-10-12T11:59:24.691030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/5/190527
 
1.1%
3/21/20055
 
0.2%
7/28/20045
 
0.2%
6/10/20045
 
0.2%
7/19/20045
 
0.2%
4/17/20045
 
0.2%
5/17/20055
 
0.2%
7/4/19055
 
0.2%
6/2/20065
 
0.2%
11/11/20045
 
0.2%
Other values (1782)2401
97.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Incident Year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)1.4%
Missing802
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean2004.363122
Minimum1904
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:24.967267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1904
5-th percentile1998
Q12003
median2006
Q32010
95-th percentile2012
Maximum2014
Range110
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.79610322
Coefficient of variation (CV)0.006384124253
Kurtosis48.19273599
Mean2004.363122
Median Absolute Deviation (MAD)3
Skewness-6.605491085
Sum4956790
Variance163.7402576
MonotonicityNot monotonic
2021-10-12T11:59:25.228485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2004295
 
9.0%
2005234
 
7.1%
2011221
 
6.7%
2010192
 
5.9%
2009185
 
5.6%
2012162
 
4.9%
2006157
 
4.8%
2003153
 
4.7%
2008142
 
4.3%
2007141
 
4.3%
Other values (24)591
18.0%
(Missing)802
24.5%
ValueCountFrequency (%)
19042
 
0.1%
190532
1.0%
19312
 
0.1%
19831
 
< 0.1%
19841
 
< 0.1%
19857
 
0.2%
19865
 
0.2%
19881
 
< 0.1%
19892
 
0.1%
19903
 
0.1%
ValueCountFrequency (%)
20142
 
0.1%
201361
 
1.9%
2012162
4.9%
2011221
6.7%
2010192
5.9%
2009185
5.6%
2008142
4.3%
2007141
4.3%
2006157
4.8%
2005234
7.1%

Incident Month
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)0.5%
Missing802
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean6.669227659
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:25.523350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.23996011
Coefficient of variation (CV)0.4858073942
Kurtosis-1.011225205
Mean6.669227659
Median Absolute Deviation (MAD)3
Skewness-0.06226190214
Sum16493
Variance10.49734151
MonotonicityNot monotonic
2021-10-12T11:59:25.835292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7288
 
8.8%
5249
 
7.6%
6233
 
7.1%
8224
 
6.8%
10218
 
6.7%
9213
 
6.5%
4197
 
6.0%
12196
 
6.0%
1168
 
5.1%
11167
 
5.1%
Other values (2)320
 
9.8%
(Missing)802
24.5%
ValueCountFrequency (%)
1168
5.1%
2160
4.9%
3160
4.9%
4197
6.0%
5249
7.6%
6233
7.1%
7288
8.8%
8224
6.8%
9213
6.5%
10218
6.7%
ValueCountFrequency (%)
12196
6.0%
11167
5.1%
10218
6.7%
9213
6.5%
8224
6.8%
7288
8.8%
6233
7.1%
5249
7.6%
4197
6.0%
3160
4.9%

Incident Day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)1.3%
Missing802
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean15.12494945
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:26.138522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.718038961
Coefficient of variation (CV)0.5764011964
Kurtosis-1.148614526
Mean15.12494945
Median Absolute Deviation (MAD)7
Skewness0.09068252606
Sum37404
Variance76.00420332
MonotonicityNot monotonic
2021-10-12T11:59:26.538137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
599
 
3.0%
1297
 
3.0%
1195
 
2.9%
492
 
2.8%
392
 
2.8%
192
 
2.8%
1491
 
2.8%
989
 
2.7%
1689
 
2.7%
687
 
2.7%
Other values (21)1550
47.3%
(Missing)802
24.5%
ValueCountFrequency (%)
192
2.8%
279
2.4%
392
2.8%
492
2.8%
599
3.0%
687
2.7%
765
2.0%
879
2.4%
989
2.7%
1079
2.4%
ValueCountFrequency (%)
3155
1.7%
3061
1.9%
2946
1.4%
2880
2.4%
2767
2.0%
2686
2.6%
2574
2.3%
2468
2.1%
2377
2.4%
2274
2.3%

Time
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct14
Distinct (%)93.3%
Missing3260
Missing (%)99.5%
Memory size102.9 KiB
8:00
10-Aug
14
18:30
4:15
Other values (9)

Length

Max length6
Median length5
Mean length4.6
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)86.7%

Sample

1st row10-Aug
2nd row14
3rd row18:30
4th row4:15
5th row17:20

Common Values

ValueCountFrequency (%)
8:002
 
0.1%
10-Aug1
 
< 0.1%
141
 
< 0.1%
18:301
 
< 0.1%
4:151
 
< 0.1%
17:201
 
< 0.1%
18:001
 
< 0.1%
18:451
 
< 0.1%
16:001
 
< 0.1%
8:301
 
< 0.1%
Other values (4)4
 
0.1%
(Missing)3260
99.5%

Length

2021-10-12T11:59:26.853522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8:002
13.3%
10-aug1
 
6.7%
141
 
6.7%
18:301
 
6.7%
4:151
 
6.7%
17:201
 
6.7%
18:001
 
6.7%
18:451
 
6.7%
16:001
 
6.7%
8:301
 
6.7%
Other values (4)4
26.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

City
Categorical

HIGH CARDINALITY
MISSING

Distinct529
Distinct (%)28.8%
Missing1438
Missing (%)43.9%
Memory size164.3 KiB
Norfolk
182 
Chesapeake
162 
Blacksburg
 
104
MRC
 
90
Lynchburg
 
79
Other values (524)
1220 

Length

Max length38
Median length10
Mean length9.451823625
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique341 ?
Unique (%)18.6%

Sample

1st rowChesapeake
2nd rowPort Broughton
3rd rowJondaryan
4th rowBallarat
5th rowBallarat

Common Values

ValueCountFrequency (%)
Norfolk182
 
5.6%
Chesapeake162
 
4.9%
Blacksburg104
 
3.2%
MRC90
 
2.7%
Lynchburg79
 
2.4%
Portsmouth51
 
1.6%
Virginia Beach47
 
1.4%
Stokes46
 
1.4%
Wellington24
 
0.7%
Spotsylvania20
 
0.6%
Other values (519)1032
31.5%
(Missing)1438
43.9%

Length

2021-10-12T11:59:27.166746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
norfolk203
 
8.5%
chesapeake182
 
7.6%
blacksburg104
 
4.4%
county96
 
4.0%
mrc90
 
3.8%
lynchburg79
 
3.3%
beach65
 
2.7%
va58
 
2.4%
portsmouth51
 
2.1%
virginia50
 
2.1%
Other values (544)1406
59.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

County
Categorical

HIGH CARDINALITY
MISSING

Distinct311
Distinct (%)11.1%
Missing477
Missing (%)14.6%
Memory size184.9 KiB
VA
842 
Auckland
160 
Wellington
 
78
Rotorua
 
68
NC
 
63
Other values (306)
1587 

Length

Max length23
Median length2
Mean length5.182273052
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)4.6%

Sample

1st rowVA
2nd rowSA
3rd rowQLD
4th rowVIC
5th rowVIC

Common Values

ValueCountFrequency (%)
VA842
25.7%
Auckland160
 
4.9%
Wellington78
 
2.4%
Rotorua68
 
2.1%
NC63
 
1.9%
NJ55
 
1.7%
FL53
 
1.6%
Clackamas41
 
1.3%
Christchurch40
 
1.2%
AL37
 
1.1%
Other values (301)1361
41.6%
(Missing)477
 
14.6%

Length

2021-10-12T11:59:27.423753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
va847
28.3%
auckland169
 
5.6%
wellington78
 
2.6%
nc71
 
2.4%
rotorua68
 
2.3%
nj55
 
1.8%
fl54
 
1.8%
clackamas41
 
1.4%
christchurch40
 
1.3%
al39
 
1.3%
Other values (291)1534
51.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EcoRegion Domain
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size210.2 KiB
Temperate
3100 
Dry
 
156
Polar
 
16
Tropical
 
2

Length

Max length9
Median length9
Mean length8.693952352
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTemperate
2nd rowDry
3rd rowTemperate
4th rowTemperate
5th rowTemperate

Common Values

ValueCountFrequency (%)
Temperate3100
94.7%
Dry156
 
4.8%
Polar16
 
0.5%
Tropical2
 
0.1%
(Missing)1
 
< 0.1%

Length

2021-10-12T11:59:27.688747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:27.890232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
temperate3100
94.7%
dry156
 
4.8%
polar16
 
0.5%
tropical2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Population Density
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.4%
Missing2145
Missing (%)65.5%
Memory size136.5 KiB
Urban
511 
Rural
367 
Suburban
144 
Wilderness
102 
Water
 
6

Length

Max length10
Median length5
Mean length5.833628319
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRural
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban511
 
15.6%
Rural367
 
11.2%
Suburban144
 
4.4%
Wilderness102
 
3.1%
Water6
 
0.2%
(Missing)2145
65.5%

Length

2021-10-12T11:59:28.039093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:28.179085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
urban511
45.2%
rural367
32.5%
suburban144
 
12.7%
wilderness102
 
9.0%
water6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Terrain
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.4%
Missing1662
Missing (%)50.7%
Memory size151.2 KiB
Flat
850 
Mountainous
388 
Hilly
355 
Water
 
17
hilly
 
2

Length

Max length11
Median length4
Mean length5.919404836
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFlat
2nd rowFlat
3rd rowFlat
4th rowFlat
5th rowFlat

Common Values

ValueCountFrequency (%)
Flat850
26.0%
Mountainous388
 
11.8%
Hilly355
 
10.8%
Water17
 
0.5%
hilly2
 
0.1%
Hilly,Flat1
 
< 0.1%
(Missing)1662
50.7%

Length

2021-10-12T11:59:28.381761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:28.601071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
flat850
52.7%
mountainous388
24.1%
hilly357
22.1%
water17
 
1.1%
hilly,flat1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Subject Category
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
Dementia
3256 
Demential
 
7
Dementia, Intellectual Disability
 
3
Dementia/Huntingtons
 
3
Demenita
 
1
Other values (5)
 
5

Length

Max length33
Median length8
Mean length8.052824427
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st rowDemenita
2nd rowDementia
3rd rowDementia
4th rowDementia
5th rowDementia

Common Values

ValueCountFrequency (%)
Dementia3256
99.4%
Demential7
 
0.2%
Dementia, Intellectual Disability3
 
0.1%
Dementia/Huntingtons3
 
0.1%
Demenita1
 
< 0.1%
Dementia AD1
 
< 0.1%
Dementia Mental Illness1
 
< 0.1%
Dementia,Hunter1
 
< 0.1%
Dementia,Mental Illness1
 
< 0.1%
Dementia,Mountain Biker1
 
< 0.1%

Length

2021-10-12T11:59:28.830024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:29.071700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
dementia3261
99.2%
demential7
 
0.2%
intellectual3
 
0.1%
disability3
 
0.1%
dementia/huntingtons3
 
0.1%
illness2
 
0.1%
demenita1
 
< 0.1%
ad1
 
< 0.1%
mental1
 
< 0.1%
dementia,hunter1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Subject Sub-Category
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct99
Distinct (%)15.6%
Missing2640
Missing (%)80.6%
Memory size125.6 KiB
Alzheimer's
268 
Dementia
65 
Alzheimers
55 
Dementia (General)
54 
walkaway
37 
Other values (94)
156 

Length

Max length93
Median length11
Mean length12.3511811
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)11.8%

Sample

1st rowParkinsons
2nd rowSuffers from psychosis
3rd rowDementia
4th rowDementia
5th rowDementia

Common Values

ValueCountFrequency (%)
Alzheimer's268
 
8.2%
Dementia65
 
2.0%
Alzheimers55
 
1.7%
Dementia (General)54
 
1.6%
walkaway37
 
1.1%
dementia20
 
0.6%
PD14
 
0.4%
Missing Alzhiemers4
 
0.1%
Memory Loss4
 
0.1%
Parkinson's4
 
0.1%
Other values (89)110
 
3.4%
(Missing)2640
80.6%

Length

2021-10-12T11:59:29.313705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alzheimer's272
30.4%
dementia157
17.5%
alzheimers65
 
7.3%
general54
 
6.0%
missing51
 
5.7%
walkaway40
 
4.5%
pd14
 
1.6%
elderly12
 
1.3%
patient9
 
1.0%
8
 
0.9%
Other values (123)214
23.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Subject Activity
Categorical

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)1.2%
Missing2164
Missing (%)66.1%
Memory size138.3 KiB
Walkaway
1084 
Hiking
 
8
Despondent
 
6
Driving
 
2
Hunting
 
2
Other values (8)
 
9

Length

Max length14
Median length8
Mean length8.00450045
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.6%

Sample

1st rowCanoeing
2nd rowDriving
3rd rowWalkaway
4th rowWalkaway
5th rowWalkaway

Common Values

ValueCountFrequency (%)
Walkaway1084
33.1%
Hiking8
 
0.2%
Despondent6
 
0.2%
Driving2
 
0.1%
Hunting2
 
0.1%
Elopement2
 
0.1%
Canoeing1
 
< 0.1%
Travelling1
 
< 0.1%
Golfing1
 
< 0.1%
Rock Hunting1
 
< 0.1%
Other values (3)3
 
0.1%
(Missing)2164
66.1%

Length

2021-10-12T11:59:29.658463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
walkaway1084
97.3%
hiking8
 
0.7%
despondent6
 
0.5%
hunting3
 
0.3%
driving2
 
0.2%
elopement2
 
0.2%
canoeing1
 
0.1%
travelling1
 
0.1%
golfing1
 
0.1%
rock1
 
0.1%
Other values (5)5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Scenario
Categorical

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)0.8%
Missing1685
Missing (%)51.5%
Memory size149.1 KiB
Lost
1093 
Missing
393 
Medical
 
33
Overdue
 
30
Investigative
 
23
Other values (8)
 
18

Length

Max length13
Median length4
Mean length5.036477987
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowTrauma
2nd rowLost
3rd rowLost
4th rowLost
5th rowLost

Common Values

ValueCountFrequency (%)
Lost1093
33.4%
Missing393
 
12.0%
Medical33
 
1.0%
Overdue30
 
0.9%
Investigative23
 
0.7%
Evading5
 
0.2%
Trauma3
 
0.1%
Drowning3
 
0.1%
Investigation2
 
0.1%
Fatality2
 
0.1%
Other values (3)3
 
0.1%
(Missing)1685
51.5%

Length

2021-10-12T11:59:29.904863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lost1093
68.7%
missing393
 
24.7%
medical33
 
2.1%
overdue30
 
1.9%
investigative23
 
1.4%
evading5
 
0.3%
trauma3
 
0.2%
drowning3
 
0.2%
investigation2
 
0.1%
fatality2
 
0.1%
Other values (3)3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Group Type
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.2%
Missing621
Missing (%)19.0%
Memory size169.9 KiB
M
1813 
F
810 
m
 
17
f
 
13
Transgender
 
1

Length

Max length11
Median length1
Mean length1.003767898
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M1813
55.4%
F810
24.7%
m17
 
0.5%
f13
 
0.4%
Transgender1
 
< 0.1%
(Missing)621
 
19.0%

Length

2021-10-12T11:59:30.168105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:30.318663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
m1830
69.0%
f823
31.0%
transgender1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

# Lost
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing418
Missing (%)12.8%
Memory size183.9 KiB
1.0
2848 
2.0
 
7
0.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02848
87.0%
2.07
 
0.2%
0.02
 
0.1%
(Missing)418
 
12.8%

Length

2021-10-12T11:59:30.513665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:30.740999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.02848
99.7%
2.07
 
0.2%
0.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct67
Distinct (%)2.7%
Missing809
Missing (%)24.7%
Infinite0
Infinite (%)0.0%
Mean75.6674777
Minimum16
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:31.022854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile56.25
Q171
median78
Q382
95-th percentile89
Maximum98
Range82
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.23487519
Coefficient of variation (CV)0.1352612179
Kurtosis3.9808863
Mean75.6674777
Median Absolute Deviation (MAD)5
Skewness-1.395891419
Sum186596
Variance104.7526701
MonotonicityNot monotonic
2021-10-12T11:59:31.357810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80189
 
5.8%
82188
 
5.7%
78125
 
3.8%
75122
 
3.7%
79118
 
3.6%
77109
 
3.3%
74103
 
3.1%
8193
 
2.8%
7683
 
2.5%
8482
 
2.5%
Other values (57)1254
38.3%
(Missing)809
24.7%
ValueCountFrequency (%)
162
0.1%
172
0.1%
182
0.1%
201
 
< 0.1%
221
 
< 0.1%
302
0.1%
342
0.1%
354
0.1%
391
 
< 0.1%
402
0.1%
ValueCountFrequency (%)
981
 
< 0.1%
971
 
< 0.1%
961
 
< 0.1%
954
 
0.1%
946
 
0.2%
9333
1.0%
9212
 
0.4%
9118
0.5%
9039
1.2%
8930
0.9%

Sex
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing621
Missing (%)19.0%
Memory size169.9 KiB
M
1832 
F
821 
Transgender
 
1

Length

Max length11
Median length1
Mean length1.003767898
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M1832
55.9%
F821
25.1%
Transgender1
 
< 0.1%
(Missing)621
 
19.0%

Length

2021-10-12T11:59:31.636867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:31.841232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
m1832
69.0%
f821
30.9%
transgender1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Weight (Kg)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct37
Distinct (%)52.1%
Missing3204
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean67.01984635
Minimum0
Maximum140
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:32.011903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.31818182
Q157.95454546
median65
Q376.13636364
95-th percentile90.90909091
Maximum140
Range140
Interquartile range (IQR)18.18181818

Descriptive statistics

Standard deviation18.35182488
Coefficient of variation (CV)0.2738267227
Kurtosis4.715238556
Mean67.01984635
Median Absolute Deviation (MAD)10
Skewness0.4086419424
Sum4758.409091
Variance336.7894765
MonotonicityNot monotonic
2021-10-12T11:59:32.318259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
709
 
0.3%
607
 
0.2%
506
 
0.2%
63.636363644
 
0.1%
553
 
0.1%
90.909090913
 
0.1%
77.272727272
 
0.1%
79.545454552
 
0.1%
61.363636362
 
0.1%
72.727272732
 
0.1%
Other values (27)31
 
0.9%
(Missing)3204
97.8%
ValueCountFrequency (%)
01
 
< 0.1%
401
 
< 0.1%
40.454545451
 
< 0.1%
40.909090911
 
< 0.1%
47.727272731
 
< 0.1%
481
 
< 0.1%
506
0.2%
521
 
< 0.1%
52.272727271
 
< 0.1%
553
0.1%
ValueCountFrequency (%)
1401
 
< 0.1%
109.09090911
 
< 0.1%
1001
 
< 0.1%
90.909090913
0.1%
902
0.1%
84.090909091
 
< 0.1%
822
0.1%
802
0.1%
79.545454552
0.1%
781
 
< 0.1%

Height (Cm)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)42.0%
Missing3194
Missing (%)97.5%
Infinite0
Infinite (%)0.0%
Mean169.5032099
Minimum144.78
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:32.613836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum144.78
5-th percentile152.4
Q1165
median170
Q3175.26
95-th percentile182.88
Maximum200
Range55.22
Interquartile range (IQR)10.26

Descriptive statistics

Standard deviation10.00044572
Coefficient of variation (CV)0.05899856248
Kurtosis0.7760560372
Mean169.5032099
Median Absolute Deviation (MAD)5.26
Skewness-0.1006793404
Sum13729.76
Variance100.0089146
MonotonicityNot monotonic
2021-10-12T11:59:32.875279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1709
 
0.3%
1656
 
0.2%
177.86
 
0.2%
1755
 
0.2%
1605
 
0.2%
1684
 
0.1%
182.884
 
0.1%
170.184
 
0.1%
167.643
 
0.1%
1783
 
0.1%
Other values (24)32
 
1.0%
(Missing)3194
97.5%
ValueCountFrequency (%)
144.781
 
< 0.1%
1452
 
0.1%
1501
 
< 0.1%
152.41
 
< 0.1%
1572
 
0.1%
157.482
 
0.1%
1581
 
< 0.1%
1605
0.2%
160.022
 
0.1%
1632
 
0.1%
ValueCountFrequency (%)
2001
 
< 0.1%
1881
 
< 0.1%
187.961
 
< 0.1%
185.421
 
< 0.1%
182.884
0.1%
1821
 
< 0.1%
180.341
 
< 0.1%
1801
 
< 0.1%
1783
0.1%
177.86
0.2%

Physical Fitness
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)1.3%
Missing2742
Missing (%)83.7%
Memory size117.6 KiB
Fair
204 
Poor
160 
Good
151 
Excellent
 
9
Unk
 
7
Other values (2)
 
2

Length

Max length9
Median length4
Mean length4.069418386
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowPoor
2nd rowFair
3rd rowPoor
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Fair204
 
6.2%
Poor160
 
4.9%
Good151
 
4.6%
Excellent9
 
0.3%
Unk7
 
0.2%
Mild1
 
< 0.1%
Mod1
 
< 0.1%
(Missing)2742
83.7%

Length

2021-10-12T11:59:33.146758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:33.440819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
fair204
38.3%
poor160
30.0%
good151
28.3%
excellent9
 
1.7%
unk7
 
1.3%
mild1
 
0.2%
mod1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mental Fitness
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.5%
Missing2159
Missing (%)65.9%
Memory size137.6 KiB
Moderate
749 
Severe
304 
Mild
 
58
Normal
 
3
Fair
 
1

Length

Max length8
Median length8
Mean length7.237455197
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowModerate
2nd rowModerate
3rd rowMild
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Moderate749
 
22.9%
Severe304
 
9.3%
Mild58
 
1.8%
Normal3
 
0.1%
Fair1
 
< 0.1%
Unknown1
 
< 0.1%
(Missing)2159
65.9%

Length

2021-10-12T11:59:33.780267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:33.992153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
moderate749
67.1%
severe304
27.2%
mild58
 
5.2%
normal3
 
0.3%
fair1
 
0.1%
unknown1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Experience
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.5%
Missing2944
Missing (%)89.9%
Memory size111.9 KiB
Fair
155 
Poor
95 
Good
67 
Excellent
 
11
Unknown
 
3

Length

Max length9
Median length4
Mean length4.193353474
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowGood
3rd rowFair
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Fair155
 
4.7%
Poor95
 
2.9%
Good67
 
2.0%
Excellent11
 
0.3%
Unknown3
 
0.1%
(Missing)2944
89.9%

Length

2021-10-12T11:59:34.212600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:34.357567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
fair155
46.8%
poor95
28.7%
good67
20.2%
excellent11
 
3.3%
unknown3
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Clothing
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.0%
Missing2873
Missing (%)87.7%
Memory size113.9 KiB
Fair
170 
Good
169 
Poor
55 
Excellent
 
8

Length

Max length9
Median length4
Mean length4.099502488
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFair
2nd rowFair
3rd rowFair
4th rowFair
5th rowFair

Common Values

ValueCountFrequency (%)
Fair170
 
5.2%
Good169
 
5.2%
Poor55
 
1.7%
Excellent8
 
0.2%
(Missing)2873
87.7%

Length

2021-10-12T11:59:34.556343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:34.733750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
fair170
42.3%
good169
42.0%
poor55
 
13.7%
excellent8
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Personality
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)27.8%
Missing3239
Missing (%)98.9%
Memory size103.7 KiB
Withdrawn
13 
Unsure
10 
Confident
Dementia
dementia
Other values (5)

Length

Max length26
Median length9
Mean length9.138888889
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)13.9%

Sample

1st rowWithdrawn
2nd rowWithdrawn
3rd rowWithdrawn
4th rowUnsure
5th rowUnsure

Common Values

ValueCountFrequency (%)
Withdrawn13
 
0.4%
Unsure10
 
0.3%
Confident3
 
0.1%
Dementia3
 
0.1%
dementia2
 
0.1%
Outgoing1
 
< 0.1%
Dementia-friendly outgoing1
 
< 0.1%
Friendly1
 
< 0.1%
Responsive/Energetic1
 
< 0.1%
suspicious of strangers1
 
< 0.1%
(Missing)3239
98.9%

Length

2021-10-12T11:59:34.952417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:35.173439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
withdrawn13
33.3%
unsure10
25.6%
dementia5
 
12.8%
confident3
 
7.7%
outgoing2
 
5.1%
dementia-friendly1
 
2.6%
friendly1
 
2.6%
responsive/energetic1
 
2.6%
suspicious1
 
2.6%
of1
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Subject Status
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.3%
Missing122
Missing (%)3.7%
Memory size193.1 KiB
Well
2428 
Injured
428 
DOA
 
153
Alive
 
105
No Trace
 
26
Other values (4)
 
13

Length

Max length19
Median length4
Mean length4.445924516
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowWell
2nd rowDOA
3rd rowInjured
4th rowWell
5th rowInjured

Common Values

ValueCountFrequency (%)
Well2428
74.1%
Injured428
 
13.1%
DOA153
 
4.7%
Alive105
 
3.2%
No Trace26
 
0.8%
Injuired10
 
0.3%
DOA,Injured1
 
< 0.1%
Injured/unconscious1
 
< 0.1%
No trace1
 
< 0.1%
(Missing)122
 
3.7%

Length

2021-10-12T11:59:35.485819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:35.747662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
well2428
76.4%
injured428
 
13.5%
doa153
 
4.8%
alive105
 
3.3%
no27
 
0.8%
trace27
 
0.8%
injuired10
 
0.3%
doa,injured1
 
< 0.1%
injured/unconscious1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Notify hours
Categorical

HIGH CARDINALITY
MISSING

Distinct373
Distinct (%)17.3%
Missing1117
Missing (%)34.1%
Memory size176.4 KiB
0.020833333
1147 
0
 
79
0.041666667
 
30
0.083333333
 
29
0.166667
 
26
Other values (368)
847 

Length

Max length11
Median length11
Mean length10.0699722
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)10.3%

Sample

1st row0.020833333
2nd row0
3rd row0.019444444
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0.0208333331147
35.0%
079
 
2.4%
0.04166666730
 
0.9%
0.08333333329
 
0.9%
0.16666726
 
0.8%
0.062522
 
0.7%
0.12518
 
0.5%
0.10416666718
 
0.5%
0.0312516
 
0.5%
0.187514
 
0.4%
Other values (363)759
23.2%
(Missing)1117
34.1%

Length

2021-10-12T11:59:36.030005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.0208333331147
53.2%
079
 
3.7%
0.04166666730
 
1.4%
0.08333333329
 
1.3%
0.16666726
 
1.2%
0.062522
 
1.0%
0.12518
 
0.8%
0.10416666718
 
0.8%
0.0312516
 
0.7%
0.187514
 
0.6%
Other values (363)759
35.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Search hours
Categorical

HIGH CARDINALITY
MISSING

Distinct562
Distinct (%)21.3%
Missing633
Missing (%)19.3%
Memory size192.8 KiB
0.000694444
 
155
0.003472222
 
84
0.010416667
 
74
0.006944444
 
70
0.013888889
 
55
Other values (557)
2204 

Length

Max length11
Median length11
Mean length9.997350492
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique302 ?
Unique (%)11.4%

Sample

1st row0.014583333
2nd row0.25
3rd row0.35
4th row0.041666667
5th row0.108333333

Common Values

ValueCountFrequency (%)
0.000694444155
 
4.7%
0.00347222284
 
2.6%
0.01041666774
 
2.3%
0.00694444470
 
2.1%
0.01388888955
 
1.7%
0.04166666755
 
1.7%
0.02083333355
 
1.7%
0.00277777854
 
1.6%
0.00138888947
 
1.4%
0.00486111143
 
1.3%
Other values (552)1950
59.5%
(Missing)633
 
19.3%

Length

2021-10-12T11:59:36.344389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.000694444155
 
5.9%
0.00347222284
 
3.2%
0.01041666774
 
2.8%
0.00694444470
 
2.6%
0.01388888955
 
2.1%
0.04166666755
 
2.1%
0.02083333355
 
2.1%
0.00277777854
 
2.0%
0.00138888947
 
1.8%
0.00486111143
 
1.6%
Other values (553)1951
73.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Time Lost
Categorical

HIGH CARDINALITY
MISSING

Distinct623
Distinct (%)27.3%
Missing996
Missing (%)30.4%
Memory size180.5 KiB
0.021527778
 
101
0.024305556
 
69
0.027777778
 
65
0.03125
 
56
0.041666667
 
50
Other values (618)
1938 

Length

Max length11
Median length11
Mean length10.04738921
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique399 ?
Unique (%)17.5%

Sample

1st row0.035416667
2nd row0.270833333
3rd row0.263888875
4th row0.861805417
5th row0.42361125

Common Values

ValueCountFrequency (%)
0.021527778101
 
3.1%
0.02430555669
 
2.1%
0.02777777865
 
2.0%
0.0312556
 
1.7%
0.04166666750
 
1.5%
0.03472222246
 
1.4%
0.02361111142
 
1.3%
0.02222222238
 
1.2%
0.02569444433
 
1.0%
0.02291666733
 
1.0%
Other values (613)1746
53.3%
(Missing)996
30.4%

Length

2021-10-12T11:59:36.652336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.021527778101
 
4.4%
0.02430555669
 
3.0%
0.02777777865
 
2.9%
0.0312556
 
2.5%
0.04166666750
 
2.2%
0.03472222246
 
2.0%
0.02361111142
 
1.8%
0.02222222238
 
1.7%
0.02569444433
 
1.4%
0.02291666733
 
1.4%
Other values (614)1747
76.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TTL Hours
Categorical

HIGH CARDINALITY

Distinct838
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size210.9 KiB
0
388 
0.021527777
 
99
0.024305555
 
67
0.027777777
 
61
0.03125
 
60
Other values (833)
2600 

Length

Max length11
Median length11
Mean length8.89129771
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique528 ?
Unique (%)16.1%

Sample

1st row0.035416666
2nd row0
3rd row0.270833333
4th row0.263888875
5th row0.861805417

Common Values

ValueCountFrequency (%)
0388
 
11.8%
0.02152777799
 
3.0%
0.02430555567
 
2.0%
0.02777777761
 
1.9%
0.0312560
 
1.8%
0.00069444453
 
1.6%
0.03472222245
 
1.4%
0.02361111143
 
1.3%
0.02222222238
 
1.2%
0.08333333334
 
1.0%
Other values (828)2387
72.9%

Length

2021-10-12T11:59:36.855967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0388
 
11.8%
0.02152777799
 
3.0%
0.02430555567
 
2.0%
0.02777777761
 
1.9%
0.0312560
 
1.8%
0.00069444453
 
1.6%
0.03472222245
 
1.4%
0.02361111143
 
1.3%
0.02222222238
 
1.2%
0.08333333334
 
1.0%
Other values (828)2387
72.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IPP Type
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)3.5%
Missing3049
Missing (%)93.1%
Memory size109.7 KiB
RESIDENCE
162 
ROAD
33 
BUILDING
 
15
FIELD
 
5
WATER
 
4
Other values (3)
 
7

Length

Max length9
Median length9
Mean length7.946902655
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWATER
2nd rowBUILDING
3rd rowRESIDENCE
4th rowRESIDENCE
5th rowRESIDENCE

Common Values

ValueCountFrequency (%)
RESIDENCE162
 
4.9%
ROAD33
 
1.0%
BUILDING15
 
0.5%
FIELD5
 
0.2%
WATER4
 
0.1%
VEHICLE3
 
0.1%
WOODS2
 
0.1%
TRAIL2
 
0.1%
(Missing)3049
93.1%

Length

2021-10-12T11:59:37.204766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:37.349212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
residence162
71.7%
road33
 
14.6%
building15
 
6.6%
field5
 
2.2%
water4
 
1.8%
vehicle3
 
1.3%
woods2
 
0.9%
trail2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IPP Classification
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)6.7%
Missing3260
Missing (%)99.5%
Memory size102.9 KiB
PLS
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPLS
2nd rowPLS
3rd rowPLS
4th rowPLS
5th rowPLS

Common Values

ValueCountFrequency (%)
PLS15
 
0.5%
(Missing)3260
99.5%

Length

2021-10-12T11:59:37.573355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:37.799578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
pls15
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IPP Coord.
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct663
Distinct (%)94.7%
Missing2575
Missing (%)78.6%
Memory size131.4 KiB
1772531, 5895989
 
4
45.499663, -122.792018
 
3
1884834, 5774036
 
3
1573115, 5183010
 
3
1885500, 5774550
 
3
Other values (658)
684 

Length

Max length33
Median length16
Mean length17.25
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique635 ?
Unique (%)90.7%

Sample

1st rowVicRoads 254 D11
2nd rowMap 16 F3
3rd row8125 04370 59757
4th row205 H10
5th rowGR871211

Common Values

ValueCountFrequency (%)
1772531, 58959894
 
0.1%
45.499663, -122.7920183
 
0.1%
1884834, 57740363
 
0.1%
1573115, 51830103
 
0.1%
1885500, 57745503
 
0.1%
2554471, 59602713
 
0.1%
1881895, 57726463
 
0.1%
1750370, 54240793
 
0.1%
1765310, 59152792
 
0.1%
1719869, 60459412
 
0.1%
Other values (653)671
 
20.5%
(Missing)2575
78.6%

Length

2021-10-12T11:59:38.000689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17725314
 
0.3%
484
 
0.3%
58959894
 
0.3%
18855004
 
0.3%
25544713
 
0.2%
303
 
0.2%
17503703
 
0.2%
57726463
 
0.2%
18818953
 
0.2%
59602713
 
0.2%
Other values (1278)1349
97.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IPPAccuracy (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing3271
Missing (%)99.9%
Memory size102.6 KiB
10m
1
649157

Length

Max length6
Median length3
Mean length3.25
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row1
2nd row10m
3rd row10m
4th row649157

Common Values

ValueCountFrequency (%)
10m2
 
0.1%
11
 
< 0.1%
6491571
 
< 0.1%
(Missing)3271
99.9%

Length

2021-10-12T11:59:38.312158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:38.512968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
10m2
50.0%
11
25.0%
6491571
25.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Destination Coord. (remove)
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing3273
Missing (%)99.9%
Memory size102.5 KiB
Unknown
36.877609, -85.6652

Length

Max length19
Median length13
Mean length13
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowUnknown
2nd row36.877609, -85.6652

Common Values

ValueCountFrequency (%)
Unknown1
 
< 0.1%
36.877609, -85.66521
 
< 0.1%
(Missing)3273
99.9%

Length

2021-10-12T11:59:38.651845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:38.782381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
unknown1
33.3%
36.8776091
33.3%
85.66521
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Temp/H
Categorical

HIGH CARDINALITY
MISSING

Distinct135
Distinct (%)11.6%
Missing2109
Missing (%)64.4%
Memory size140.3 KiB
21.11111111
 
84
26.66666667
 
73
10
 
69
4.444444444
 
51
15.55555556
 
42
Other values (130)
847 

Length

Max length13
Median length11
Mean length8.20754717
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)2.7%

Sample

1st row35
2nd row15
3rd row18
4th row20
5th row30

Common Values

ValueCountFrequency (%)
21.1111111184
 
2.6%
26.6666666773
 
2.2%
1069
 
2.1%
4.44444444451
 
1.6%
15.5555555642
 
1.3%
23.8888888937
 
1.1%
2034
 
1.0%
1533
 
1.0%
18.3333333329
 
0.9%
32.2222222229
 
0.9%
Other values (125)685
 
20.9%
(Missing)2109
64.4%

Length

2021-10-12T11:59:38.974356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21.1111111184
 
7.2%
26.6666666773
 
6.2%
1069
 
5.9%
4.44444444451
 
4.4%
15.5555555642
 
3.6%
23.8888888937
 
3.2%
1535
 
3.0%
2034
 
2.9%
18.3333333329
 
2.5%
32.2222222229
 
2.5%
Other values (111)685
58.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Temp L
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)33.1%
Missing3127
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean11.72594595
Minimum-9.444444444
Maximum38
Zeros4
Zeros (%)0.1%
Negative17
Negative (%)0.5%
Memory size25.7 KiB
2021-10-12T11:59:39.317484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-9.444444444
5-th percentile-1.072222222
Q13
median10
Q318
95-th percentile31.3
Maximum38
Range47.44444444
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.64223461
Coefficient of variation (CV)0.9075800503
Kurtosis-0.2564820566
Mean11.72594595
Median Absolute Deviation (MAD)7
Skewness0.5483640518
Sum1735.44
Variance113.2571575
MonotonicityNot monotonic
2021-10-12T11:59:39.712651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
319
 
0.6%
1311
 
0.3%
108
 
0.2%
188
 
0.2%
-18
 
0.2%
87
 
0.2%
56
 
0.2%
255
 
0.2%
15.555555565
 
0.2%
205
 
0.2%
Other values (39)66
 
2.0%
(Missing)3127
95.5%
ValueCountFrequency (%)
-9.4444444441
 
< 0.1%
-6.6666666672
 
0.1%
-5.5555555561
 
< 0.1%
-3.8888888892
 
0.1%
-3.3333333331
 
< 0.1%
-1.1111111111
 
< 0.1%
-18
0.2%
-0.5555555561
 
< 0.1%
04
0.1%
1.6666666672
 
0.1%
ValueCountFrequency (%)
384
0.1%
37.777777781
 
< 0.1%
331
 
< 0.1%
32.222222221
 
< 0.1%
321
 
< 0.1%
302
0.1%
29.444444441
 
< 0.1%
291
 
< 0.1%
28.888888891
 
< 0.1%
26.666666672
0.1%

Wind (kph) (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct46
Distinct (%)21.7%
Missing3063
Missing (%)93.5%
Memory size108.0 KiB
0
59 
10
16 
6
16 
8
13 
15
12 
Other values (41)
96 

Length

Max length7
Median length1
Mean length1.655660377
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)11.3%

Sample

1st row31
2nd row12
3rd row10
4th row0
5th row10

Common Values

ValueCountFrequency (%)
059
 
1.8%
1016
 
0.5%
616
 
0.5%
813
 
0.4%
1512
 
0.4%
59
 
0.3%
187
 
0.2%
426
 
0.2%
255
 
0.2%
165
 
0.2%
Other values (36)64
 
2.0%
(Missing)3063
93.5%

Length

2021-10-12T11:59:40.024163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
059
27.8%
616
 
7.5%
1016
 
7.5%
813
 
6.1%
1512
 
5.7%
59
 
4.2%
187
 
3.3%
426
 
2.8%
165
 
2.4%
205
 
2.4%
Other values (34)64
30.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Weather
Categorical

HIGH CARDINALITY
MISSING

Distinct287
Distinct (%)18.3%
Missing1710
Missing (%)52.2%
Memory size151.6 KiB
Clear
404 
Clear
170 
clear,
111 
Rain
81 
Overcast
 
47
Other values (282)
752 

Length

Max length62
Median length6
Mean length7.101597444
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184 ?
Unique (%)11.8%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowOvercast

Common Values

ValueCountFrequency (%)
Clear404
 
12.3%
Clear 170
 
5.2%
clear, 111
 
3.4%
Rain81
 
2.5%
Overcast47
 
1.4%
Cloudy36
 
1.1%
sunny, 33
 
1.0%
Partly cloudy27
 
0.8%
Rain 20
 
0.6%
cool, 19
 
0.6%
Other values (277)617
 
18.8%
(Missing)1710
52.2%

Length

2021-10-12T11:59:40.429136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear834
43.0%
rain141
 
7.3%
cloudy119
 
6.1%
sunny117
 
6.0%
s111
 
5.7%
cool66
 
3.4%
overcast58
 
3.0%
cold51
 
2.6%
hot42
 
2.2%
warm41
 
2.1%
Other values (102)359
18.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Snow (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)3.8%
Missing2955
Missing (%)90.2%
Memory size110.9 KiB
No
261 
0
39 
nil
 
5
Nil
 
4
1
 
3
Other values (7)
 
8

Length

Max length7
Median length2
Mean length1.94375
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.9%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No261
 
8.0%
039
 
1.2%
nil5
 
0.2%
Nil4
 
0.1%
13
 
0.1%
light2
 
0.1%
no1
 
< 0.1%
on tops1
 
< 0.1%
y1
 
< 0.1%
Snow1
 
< 0.1%
Other values (2)2
 
0.1%
(Missing)2955
90.2%

Length

2021-10-12T11:59:40.701444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no262
81.6%
039
 
12.1%
nil10
 
3.1%
13
 
0.9%
light2
 
0.6%
snow2
 
0.6%
on1
 
0.3%
tops1
 
0.3%
y1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Rain (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)5.3%
Missing2825
Missing (%)86.3%
Memory size114.5 KiB
No
303 
Yes
55 
0
 
30
None
 
13
nil
 
8
Other values (19)
41 

Length

Max length16
Median length2
Mean length2.426666667
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)2.4%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No303
 
9.3%
Yes55
 
1.7%
030
 
0.9%
None13
 
0.4%
nil8
 
0.2%
Light6
 
0.2%
Heavy6
 
0.2%
Nil4
 
0.1%
Drizzle4
 
0.1%
104
 
0.1%
Other values (14)17
 
0.5%
(Missing)2825
86.3%

Length

2021-10-12T11:59:40.968420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no303
66.9%
yes55
 
12.1%
030
 
6.6%
none13
 
2.9%
nil13
 
2.9%
heavy8
 
1.8%
light7
 
1.5%
drizzle7
 
1.5%
showers4
 
0.9%
104
 
0.9%
Other values (8)9
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Light (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)27.3%
Missing3264
Missing (%)99.7%
Memory size102.8 KiB
Day
Day+Night
Twilight

Length

Max length9
Median length8
Mean length6.545454545
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDay
2nd rowDay+Night
3rd rowDay
4th rowDay+Night
5th rowDay+Night

Common Values

ValueCountFrequency (%)
Day4
 
0.1%
Day+Night4
 
0.1%
Twilight3
 
0.1%
(Missing)3264
99.7%

Length

2021-10-12T11:59:42.608963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:42.778172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
day4
36.4%
day+night4
36.4%
twilight3
27.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Investigative Find (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)3.7%
Missing3111
Missing (%)95.0%
Memory size106.9 KiB
Yes
75 
No
67 
no
19 
Left area
 
1
Taxi
 
1

Length

Max length9
Median length2
Mean length2.536585366
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.8%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Yes75
 
2.3%
No67
 
2.0%
no19
 
0.6%
Left area1
 
< 0.1%
Taxi1
 
< 0.1%
Church1
 
< 0.1%
(Missing)3111
95.0%

Length

2021-10-12T11:59:43.026779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:43.237148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no86
52.1%
yes75
45.5%
left1
 
0.6%
area1
 
0.6%
taxi1
 
0.6%
church1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Inv Find Details (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)60.0%
Missing3265
Missing (%)99.7%
Memory size102.8 KiB
Bus
Hospital
Took transportation
runaway
With family

Length

Max length19
Median length7.5
Mean length8.5
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st rowHospital
2nd rowrunaway
3rd rowTook transportation
4th rowTook transportation
5th rowWith family

Common Values

ValueCountFrequency (%)
Bus3
 
0.1%
Hospital2
 
0.1%
Took transportation2
 
0.1%
runaway1
 
< 0.1%
With family1
 
< 0.1%
ride1
 
< 0.1%
(Missing)3265
99.7%

Length

2021-10-12T11:59:43.423239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:43.587664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
bus3
23.1%
hospital2
15.4%
took2
15.4%
transportation2
15.4%
runaway1
 
7.7%
with1
 
7.7%
family1
 
7.7%
ride1
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Suspended (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)9.9%
Missing3174
Missing (%)96.9%
Memory size105.4 KiB
No
67 
Yes
22 
Authority Decision
 
3
Not in Area
 
2
Lack of clues
 
2
Other values (5)
 
5

Length

Max length118
Median length2
Mean length4.920792079
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No67
 
2.0%
Yes22
 
0.7%
Authority Decision3
 
0.1%
Not in Area2
 
0.1%
Lack of clues2
 
0.1%
Suspended1
 
< 0.1%
Out of Area1
 
< 0.1%
LACK CLUES / FAMILY DECISION1
 
< 0.1%
No Clues, Exhausted Resources1
 
< 0.1%
Seach covered a high POD witness reports that he was at a truckstop on a nearby interstee lead to high ROW possibility1
 
< 0.1%
(Missing)3174
96.9%

Length

2021-10-12T11:59:43.796742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:44.043433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no68
47.9%
yes22
 
15.5%
decision4
 
2.8%
clues4
 
2.8%
authority3
 
2.1%
area3
 
2.1%
lack3
 
2.1%
of3
 
2.1%
a3
 
2.1%
not2
 
1.4%
Other values (25)27
 
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Subject Found feature
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct59
Distinct (%)3.5%
Missing1575
Missing (%)48.1%
Memory size161.7 KiB
Residence
488 
Building/developed area
335 
Road
246 
Forest
124 
Structure
100 
Other values (54)
407 

Length

Max length35
Median length9
Mean length10.66235294
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)1.6%

Sample

1st rowWater
2nd rowRoad
3rd rowRoad
4th rowWater
5th rowRoad

Common Values

ValueCountFrequency (%)
Residence488
 
14.9%
Building/developed area335
 
10.2%
Road246
 
7.5%
Forest124
 
3.8%
Structure100
 
3.1%
Field64
 
2.0%
Water39
 
1.2%
Vehicle34
 
1.0%
Drainage33
 
1.0%
Linear33
 
1.0%
Other values (49)204
 
6.2%
(Missing)1575
48.1%

Length

2021-10-12T11:59:44.330382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
residence491
22.7%
area345
16.0%
building/developed340
15.7%
road246
11.4%
forest133
 
6.1%
structure100
 
4.6%
field65
 
3.0%
water44
 
2.0%
41
 
1.9%
vehicle34
 
1.6%
Other values (55)324
15.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Found Secondary (remove)
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct84
Distinct (%)39.6%
Missing3063
Missing (%)93.5%
Memory size110.2 KiB
None/Grass
25 
Building
14 
Wall / Fence Line
14 
Medium
 
13
Parking Lot
 
13
Other values (79)
133 

Length

Max length145
Median length8
Mean length12.55660377
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)28.8%

Sample

1st rowMangroves
2nd rowBuilding
3rd rowBus shelter
4th rowBuilding
5th rowChurch

Common Values

ValueCountFrequency (%)
None/Grass25
 
0.8%
Building14
 
0.4%
Wall / Fence Line14
 
0.4%
Medium13
 
0.4%
Parking Lot13
 
0.4%
Railroad9
 
0.3%
City9
 
0.3%
Sparse8
 
0.2%
Bus6
 
0.2%
Stream5
 
0.2%
Other values (74)96
 
2.9%
(Missing)3063
93.5%

Length

2021-10-12T11:59:44.673457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none/grass25
 
5.6%
16
 
3.6%
fence15
 
3.4%
building14
 
3.2%
wall14
 
3.2%
line14
 
3.2%
medium13
 
2.9%
parking13
 
2.9%
lot13
 
2.9%
city12
 
2.7%
Other values (185)294
66.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mobility Responsiveness (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)6.9%
Missing3131
Missing (%)95.6%
Memory size107.2 KiB
Mobile
93 
Immobile
25 
non-responsive
 
5
Mobile and unresponsive
 
5
Mobile and Unresponsive
 
5
Other values (5)
11 

Length

Max length25
Median length6
Mean length8.986111111
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st rowImmobile
2nd rowMobile
3rd rowMobile
4th rowImmobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile93
 
2.8%
Immobile25
 
0.8%
non-responsive5
 
0.2%
Mobile and unresponsive5
 
0.2%
Mobile and Unresponsive5
 
0.2%
Mobile and responsive4
 
0.1%
Immobile and Unresponsive3
 
0.1%
Immobile and unresponsive2
 
0.1%
Responsive1
 
< 0.1%
Mobile/Responsive1
 
< 0.1%
(Missing)3131
95.6%

Length

2021-10-12T11:59:44.920750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:45.145831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mobile107
58.8%
immobile30
 
16.5%
and19
 
10.4%
unresponsive15
 
8.2%
non-responsive5
 
2.7%
responsive5
 
2.7%
mobile/responsive1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lost Strategy
Categorical

HIGH CARDINALITY
MISSING

Distinct107
Distinct (%)24.1%
Missing2831
Missing (%)86.4%
Memory size123.6 KiB
Random wandering
103 
Wandered
48 
None
44 
Travel Aid
29 
Stayed Put
 
16
Other values (102)
204 

Length

Max length253
Median length11
Mean length23.58333333
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)18.5%

Sample

1st rowTravel Aid
2nd rowTravel Aid
3rd rowStayed Put
4th rowTravel Aid
5th rowStayed Put

Common Values

ValueCountFrequency (%)
Random wandering103
 
3.1%
Wandered48
 
1.5%
None44
 
1.3%
Travel Aid29
 
0.9%
Stayed Put16
 
0.5%
Nil15
 
0.5%
Maintained one specific route12
 
0.4%
Nothing11
 
0.3%
Other11
 
0.3%
Staying put9
 
0.3%
Other values (97)146
 
4.5%
(Missing)2831
86.4%

Length

2021-10-12T11:59:45.497348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
random113
 
6.6%
wandering103
 
6.0%
wandered49
 
2.9%
to47
 
2.8%
none45
 
2.6%
travel34
 
2.0%
aid34
 
2.0%
a28
 
1.6%
put27
 
1.6%
was26
 
1.5%
Other values (456)1200
70.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distance IPP (km)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct479
Distinct (%)20.0%
Missing883
Missing (%)27.0%
Infinite0
Infinite (%)0.0%
Mean3.080007375
Minimum0
Maximum316.895072
Zeros211
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:45.802865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.39624076
median1
Q33.006827603
95-th percentile11.26542962
Maximum316.895072
Range316.895072
Interquartile range (IQR)2.610586843

Descriptive statistics

Standard deviation9.592975975
Coefficient of variation (CV)3.114595132
Kurtosis526.063606
Mean3.080007375
Median Absolute Deviation (MAD)0.847599708
Skewness18.83551717
Sum7367.37764
Variance92.02518805
MonotonicityNot monotonic
2021-10-12T11:59:46.187235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0211
 
6.4%
0.914401754110
 
3.4%
0.3962407695
 
2.9%
1.60934708879
 
2.4%
160
 
1.8%
1.21920233959
 
1.8%
0.80554
 
1.6%
0.16148
 
1.5%
0.546
 
1.4%
246
 
1.4%
Other values (469)1584
48.4%
(Missing)883
27.0%
ValueCountFrequency (%)
0211
6.4%
7.62 × 10-51
 
< 0.1%
0.0003048011
 
< 0.1%
0.0004081
 
< 0.1%
0.0004572011
 
< 0.1%
0.00131
 
< 0.1%
0.0030480061
 
< 0.1%
0.0051
 
< 0.1%
0.0060960122
 
0.1%
0.0076200154
 
0.1%
ValueCountFrequency (%)
316.8950721
< 0.1%
152.40029241
< 0.1%
128.7477671
< 0.1%
104.01319961
< 0.1%
82.076701481
< 0.1%
751
< 0.1%
72.420618951
< 0.1%
64.373883521
< 0.1%
50.694433271
< 0.1%
48.31
< 0.1%

Distance Destination (km) (remove)
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing3274
Missing (%)> 99.9%
Memory size128.1 KiB
7.00313428

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row7.00313428

Common Values

ValueCountFrequency (%)
7.003134281
 
< 0.1%
(Missing)3274
> 99.9%

Length

2021-10-12T11:59:46.480050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:46.680119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
7.003134281
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distance IPP (miles) (remove)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct284
Distinct (%)15.7%
Missing1465
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean1.924397871
Minimum0
Maximum196.9090909
Zeros173
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:46.856862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.246212121
median0.568181818
Q31.86
95-th percentile6.864
Maximum196.9090909
Range196.9090909
Interquartile range (IQR)1.613787879

Descriptive statistics

Standard deviation6.572008441
Coefficient of variation (CV)3.415098583
Kurtosis469.0758904
Mean1.924397871
Median Absolute Deviation (MAD)0.479781818
Skewness18.30860218
Sum3483.160146
Variance43.19129495
MonotonicityNot monotonic
2021-10-12T11:59:47.177502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0173
 
5.3%
1119
 
3.6%
0.568181818110
 
3.4%
0.24621212195
 
2.9%
0.570
 
2.1%
259
 
1.8%
0.75757575859
 
1.8%
0.148
 
1.5%
344
 
1.3%
540
 
1.2%
Other values (274)993
30.3%
(Missing)1465
44.7%
ValueCountFrequency (%)
0173
5.3%
4.73 × 10-51
 
< 0.1%
0.0001893941
 
< 0.1%
0.0002840911
 
< 0.1%
0.0018939391
 
< 0.1%
0.0037878792
 
0.1%
0.0047348484
 
0.1%
0.0056818181
 
< 0.1%
0.0068181821
 
< 0.1%
0.0094696974
 
0.1%
ValueCountFrequency (%)
196.90909091
< 0.1%
94.69696971
< 0.1%
801
< 0.1%
64.630681821
< 0.1%
511
< 0.1%
451
< 0.1%
401
< 0.1%
31.51
< 0.1%
301
< 0.1%
281
< 0.1%

Track Offset (m)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct109
Distinct (%)9.3%
Missing2101
Missing (%)64.2%
Infinite0
Infinite (%)0.0%
Mean101.1648422
Minimum0
Maximum12874.90856
Zeros437
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:47.547449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.572055596
Q330.48037064
95-th percentile396.2448183
Maximum12874.90856
Range12874.90856
Interquartile range (IQR)30.48037064

Descriptive statistics

Standard deviation537.60697
Coefficient of variation (CV)5.314168029
Kurtosis321.4465726
Mean101.1648422
Median Absolute Deviation (MAD)4.572055596
Skewness15.76627666
Sum118767.5247
Variance289021.2542
MonotonicityNot monotonic
2021-10-12T11:59:47.914763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0437
 
13.3%
15.2401853275
 
2.3%
30.4803706452
 
1.6%
3.04803706447
 
1.4%
91.4411119232
 
1.0%
9.14411119226
 
0.8%
6.09607412825
 
0.8%
1.52401853224
 
0.7%
1023
 
0.7%
322
 
0.7%
Other values (99)411
 
12.5%
(Missing)2101
64.2%
ValueCountFrequency (%)
0437
13.3%
0.011
 
< 0.1%
0.12
 
0.1%
0.31
 
< 0.1%
0.3048037063
 
0.1%
0.457205561
 
< 0.1%
0.6096074137
 
0.2%
0.91441111912
 
0.4%
18
 
0.2%
1.2192148263
 
0.1%
ValueCountFrequency (%)
12874.908561
 
< 0.1%
8046.8178491
 
< 0.1%
50001
 
< 0.1%
3962.4481831
 
< 0.1%
3200.4389171
 
< 0.1%
2377.468912
 
0.1%
1828.8222381
 
< 0.1%
1609.363579
0.3%
16001
 
< 0.1%
1524.0185321
 
< 0.1%

Find Coord
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct434
Distinct (%)97.3%
Missing2829
Missing (%)86.4%
Memory size118.6 KiB
2554571
 
3
0
 
3
45.502737, -122.798013
 
3
1757286
 
2
1853085
 
2
Other values (429)
433 

Length

Max length39
Median length7
Mean length12.09865471
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique425 ?
Unique (%)95.3%

Sample

1st rowMap 16 G5
2nd row8125 04365 59742
3rd row821241
4th row205 J11
5th rowGR821239

Common Values

ValueCountFrequency (%)
25545713
 
0.1%
03
 
0.1%
45.502737, -122.7980133
 
0.1%
17572862
 
0.1%
18530852
 
0.1%
17553282
 
0.1%
2952
 
0.1%
954/7752
 
0.1%
45.150825, -122.57177692
 
0.1%
17513931
 
< 0.1%
Other values (424)424
 
12.9%
(Missing)2829
86.4%

Length

2021-10-12T11:59:48.219063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25545713
 
0.5%
122.7980133
 
0.5%
03
 
0.5%
45.5027373
 
0.5%
18530852
 
0.3%
17553282
 
0.3%
2952
 
0.3%
954/7752
 
0.3%
45.1508252
 
0.3%
122.57177692
 
0.3%
Other values (575)577
96.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Find Accuracy
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct262
Distinct (%)97.8%
Missing3007
Missing (%)91.8%
Memory size110.7 KiB
0
 
3
5960671
 
3
5914957
 
2
5854078
 
2
5426749
 
1
Other values (257)
257 

Length

Max length9
Median length7
Mean length6.611940299
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique258 ?
Unique (%)96.3%

Sample

1st row1
2nd row5741710
3rd row657
4th row5984459
5th row5757860

Common Values

ValueCountFrequency (%)
03
 
0.1%
59606713
 
0.1%
59149572
 
0.1%
58540782
 
0.1%
54267491
 
< 0.1%
63484751
 
< 0.1%
54026881
 
< 0.1%
58199451
 
< 0.1%
59233481
 
< 0.1%
59175631
 
< 0.1%
Other values (252)252
 
7.7%
(Missing)3007
91.8%

Length

2021-10-12T11:59:48.507575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03
 
1.1%
59606713
 
1.1%
59149572
 
0.7%
58540782
 
0.7%
64778691
 
0.4%
64918961
 
0.4%
64718381
 
0.4%
64746831
 
0.4%
64681611
 
0.4%
64676021
 
0.4%
Other values (253)253
94.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Disperson Angle (remove)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing3273
Missing (%)99.9%
Memory size128.1 KiB
11.57518882
153.4349488

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row11.57518882
2nd row153.4349488

Common Values

ValueCountFrequency (%)
11.575188821
 
< 0.1%
153.43494881
 
< 0.1%
(Missing)3273
99.9%

Length

2021-10-12T11:59:48.783482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:48.914771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
11.575188821
50.0%
153.43494881
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mission Close (remove)
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)3.1%
Missing3115
Missing (%)95.1%
Memory size107.3 KiB
Found
149 
No Information
 
5
Authority Decision
 
4
Not Found
 
1
Nothing Found
 
1

Length

Max length18
Median length5
Mean length5.68125
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st rowFound
2nd rowFound
3rd rowFound
4th rowFound
5th rowFound

Common Values

ValueCountFrequency (%)
Found149
 
4.5%
No Information5
 
0.2%
Authority Decision4
 
0.1%
Not Found1
 
< 0.1%
Nothing Found1
 
< 0.1%
(Missing)3115
95.1%

Length

2021-10-12T11:59:49.054763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:49.245821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
found151
88.3%
no5
 
2.9%
information5
 
2.9%
authority4
 
2.3%
decision4
 
2.3%
not1
 
0.6%
nothing1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Rescue Method
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)16.5%
Missing3166
Missing (%)96.7%
Memory size106.8 KiB
Assist/own power
46 
Vehicle evacuation
20 
Carry out by foot
13 
Walkout
Assist/own power~Vehicle evacuation
Other values (13)
20 

Length

Max length35
Median length16
Mean length15.72477064
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)8.3%

Sample

1st rowAssist/own power
2nd rowCarry out by foot
3rd rowVehicle evacuation
4th rowAssist/own power
5th rowVehicle evacuation

Common Values

ValueCountFrequency (%)
Assist/own power46
 
1.4%
Vehicle evacuation20
 
0.6%
Carry out by foot13
 
0.4%
Walkout5
 
0.2%
Assist/own power~Vehicle evacuation5
 
0.2%
Own Power4
 
0.1%
Vehicle3
 
0.1%
Helicopter2
 
0.1%
Carryout2
 
0.1%
Carry Out1
 
< 0.1%
Other values (8)8
 
0.2%
(Missing)3166
96.7%

Length

2021-10-12T11:59:49.543863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
assist/own51
21.9%
power50
21.5%
evacuation26
11.2%
vehicle23
9.9%
carry14
 
6.0%
out14
 
6.0%
by13
 
5.6%
foot13
 
5.6%
walkout5
 
2.1%
power~vehicle5
 
2.1%
Other values (14)19
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Resources Used
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)42.9%
Missing3254
Missing (%)99.4%
Memory size103.8 KiB
Police Officers.
Police Officers., Family/volunteers.
Police Officers., State Emergency Service., Family/volunteers., Helicopter.
Police Officers., State Emergency Service., Helicopter., Fixed wing aircraft., Other Gov Departments eg National Parks Officers
Police Officers., State Emergency Service., Family/volunteers.
Other values (4)

Length

Max length127
Median length36
Mean length41.0952381
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)33.3%

Sample

1st rowPolice Officers.
2nd rowPolice Officers.
3rd rowPolice Officers., State Emergency Service., Family/volunteers., Helicopter.
4th rowPolice Officers., State Emergency Service., Helicopter., Fixed wing aircraft., Other Gov Departments eg National Parks Officers
5th rowPolice Officers.

Common Values

ValueCountFrequency (%)
Police Officers.9
 
0.3%
Police Officers., Family/volunteers.5
 
0.2%
Police Officers., State Emergency Service., Family/volunteers., Helicopter.1
 
< 0.1%
Police Officers., State Emergency Service., Helicopter., Fixed wing aircraft., Other Gov Departments eg National Parks Officers1
 
< 0.1%
Police Officers., State Emergency Service., Family/volunteers.1
 
< 0.1%
Police Officers., State Emergency Service., Helicopter., Volunteer water rescue1
 
< 0.1%
Police Officers., State Emergency Service.1
 
< 0.1%
Police Officers., State Emergency Service., Family/volunteers., Helicopter., Other Gov Departments eg National Parks Officers1
 
< 0.1%
Police Officers., Helicopter.1
 
< 0.1%
(Missing)3254
99.4%

Length

2021-10-12T11:59:49.769248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-12T11:59:49.961582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
officers23
24.7%
police21
22.6%
family/volunteers8
 
8.6%
state6
 
6.5%
emergency6
 
6.5%
service6
 
6.5%
helicopter5
 
5.4%
parks2
 
2.2%
other2
 
2.2%
gov2
 
2.2%
Other values (9)12
12.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Air Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct18
Distinct (%)4.6%
Missing2880
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean0.3194936709
Minimum0
Maximum20
Zeros345
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:50.306480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.406511235
Coefficient of variation (CV)4.402313295
Kurtosis106.7534013
Mean0.3194936709
Median Absolute Deviation (MAD)0
Skewness8.969921736
Sum126.2
Variance1.978273855
MonotonicityNot monotonic
2021-10-12T11:59:50.606654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0345
 
10.5%
112
 
0.4%
38
 
0.2%
0.57
 
0.2%
26
 
0.2%
0.42
 
0.1%
1.52
 
0.1%
2.82
 
0.1%
52
 
0.1%
0.91
 
< 0.1%
Other values (8)8
 
0.2%
(Missing)2880
87.9%
ValueCountFrequency (%)
0345
10.5%
0.42
 
0.1%
0.57
 
0.2%
0.91
 
< 0.1%
112
 
0.4%
1.21
 
< 0.1%
1.52
 
0.1%
26
 
0.2%
2.11
 
< 0.1%
2.51
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
10.31
 
< 0.1%
8.31
 
< 0.1%
5.91
 
< 0.1%
52
 
0.1%
4.11
 
< 0.1%
38
0.2%
2.82
 
0.1%
2.51
 
< 0.1%
2.11
 
< 0.1%

Total Personnel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct78
Distinct (%)10.6%
Missing2540
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean16.96326531
Minimum0
Maximum1857
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:51.021098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q315
95-th percentile56
Maximum1857
Range1857
Interquartile range (IQR)11

Descriptive statistics

Standard deviation71.8126537
Coefficient of variation (CV)4.233421597
Kurtosis589.3424198
Mean16.96326531
Median Absolute Deviation (MAD)4
Skewness23.12213305
Sum12468
Variance5157.057232
MonotonicityNot monotonic
2021-10-12T11:59:51.379888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666
 
2.0%
364
 
2.0%
458
 
1.8%
555
 
1.7%
148
 
1.5%
240
 
1.2%
738
 
1.2%
930
 
0.9%
829
 
0.9%
1225
 
0.8%
Other values (68)282
 
8.6%
(Missing)2540
77.6%
ValueCountFrequency (%)
018
 
0.5%
148
1.5%
240
1.2%
364
2.0%
458
1.8%
555
1.7%
666
2.0%
738
1.2%
829
0.9%
930
0.9%
ValueCountFrequency (%)
18571
< 0.1%
2161
< 0.1%
2101
< 0.1%
1941
< 0.1%
1741
< 0.1%
1491
< 0.1%
1421
< 0.1%
1331
< 0.1%
1251
< 0.1%
1201
< 0.1%

Total Man Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct310
Distinct (%)30.9%
Missing2271
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean76.42832769
Minimum0
Maximum6600
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:51.806495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median12
Q338.7
95-th percentile295.65
Maximum6600
Range6600
Interquartile range (IQR)34.7

Descriptive statistics

Standard deviation319.3801666
Coefficient of variation (CV)4.178819245
Kurtosis199.126412
Mean76.42832769
Median Absolute Deviation (MAD)10
Skewness12.09638111
Sum76734.041
Variance102003.6908
MonotonicityNot monotonic
2021-10-12T11:59:52.212321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163
 
1.9%
254
 
1.6%
340
 
1.2%
437
 
1.1%
535
 
1.1%
635
 
1.1%
824
 
0.7%
1223
 
0.7%
1022
 
0.7%
920
 
0.6%
Other values (300)651
 
19.9%
(Missing)2271
69.3%
ValueCountFrequency (%)
016
 
0.5%
0.251
 
< 0.1%
0.59
 
0.3%
0.61
 
< 0.1%
0.651
 
< 0.1%
0.71
 
< 0.1%
0.752
 
0.1%
0.82
 
0.1%
163
1.9%
1.081
 
< 0.1%
ValueCountFrequency (%)
66001
< 0.1%
33121
< 0.1%
25601
< 0.1%
25271
< 0.1%
24011
< 0.1%
2371.61
< 0.1%
22201
< 0.1%
1794.41
< 0.1%
17251
< 0.1%
12501
< 0.1%

Total Cost
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct286
Distinct (%)33.8%
Missing2428
Missing (%)74.1%
Infinite0
Infinite (%)0.0%
Mean949.1198471
Minimum0
Maximum74997.999
Zeros211
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2021-10-12T11:59:52.551531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.505
median208.25
Q3721.4375
95-th percentile3761.675
Maximum74997.999
Range74997.999
Interquartile range (IQR)709.9325

Descriptive statistics

Standard deviation3436.290867
Coefficient of variation (CV)3.620502593
Kurtosis265.1295962
Mean949.1198471
Median Absolute Deviation (MAD)208.25
Skewness13.82057655
Sum803904.5105
Variance11808094.93
MonotonicityNot monotonic
2021-10-12T11:59:52.872905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0211
 
6.4%
80.7559
 
1.8%
144.531
 
0.9%
161.524
 
0.7%
208.2520
 
0.6%
10513
 
0.4%
242.2512
 
0.4%
335.7511
 
0.3%
7011
 
0.3%
21010
 
0.3%
Other values (276)445
 
13.6%
(Missing)2428
74.1%
ValueCountFrequency (%)
0211
6.4%
101
 
< 0.1%
13.011
 
< 0.1%
21.581
 
< 0.1%
22.281
 
< 0.1%
352
 
0.1%
36.561
 
< 0.1%
37.51
 
< 0.1%
40.3753
 
0.1%
41.51
 
< 0.1%
ValueCountFrequency (%)
74997.9991
< 0.1%
26604.56651
< 0.1%
229671
< 0.1%
225251
< 0.1%
219131
< 0.1%
17970.981
< 0.1%
168471
< 0.1%
146031
< 0.1%
12284.75251
< 0.1%
116111
< 0.1%

Comments
Categorical

HIGH CARDINALITY
MISSING

Distinct870
Distinct (%)91.2%
Missing2321
Missing (%)70.9%
Memory size342.5 KiB
[See report for details.]
 
51
NR
 
9
no
 
7
Nil
 
6
No
 
5
Other values (865)
876 

Length

Max length3732
Median length114
Mean length232.1226415
Min length2

Characters and Unicode

Total characters1117
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique855 ?
Unique (%)89.6%

Sample

1st row71 year old male suffering Alzheimers, canoeing in bay. Deceased; found by helicopter.
2nd rowLeft at 0630 to drive 14km to Jondaryan. Drove through. Ran out of fuel 260 km later, still believed he was heading for Jondaryan. Located 4km E. of Wallumbilla.
3rd rowDescp broadcast to taxis, trucks, etc. MP located by taxi driver.
4th row88 year old male suffering dementia, wandered from Geriatric Centre. Suffering mild hyperthermia when found. Vic Roads 256 J21 to 257 L22, approx. 1000m.
5th rowAlzheimers patient wandered from hospital, diabetic, partially blind. Found by Mobile Police Patrol. [Moved local coord to LKP coord.]

Common Values

ValueCountFrequency (%)
[See report for details.]51
 
1.6%
NR9
 
0.3%
no7
 
0.2%
Nil6
 
0.2%
No5
 
0.2%
none3
 
0.1%
84 year old male suffering dementia wandered.2
 
0.1%
On April 30th, 2009 at 1942 hours, myself and several other deputies responded to [address]. [Reporting party] reported that her father, [subject], has dementia and had wandered away from the residence. [Reporting party] stated that her father had been gone for approximately a half an hour. [subject] is a client of Project Lifesaver, SAR was activated and we attempted to locate the subject by his Project Lifesaver transmitter bracelet. [subject] was located at 2207 hours by an off duty deputy 7.8 miles away from his residence. The subject was found unharmed and was returned to the residence.2
 
0.1%
None2
 
0.1%
Missing 66yo Alzheimer's Walkawy2
 
0.1%
Other values (860)865
 
26.4%
(Missing)2321
70.9%

Length

2021-10-12T11:59:53.234756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1527
 
4.0%
and1150
 
3.0%
to1098
 
2.9%
was945
 
2.5%
a772
 
2.0%
in681
 
1.8%
from549
 
1.5%
subject527
 
1.4%
of510
 
1.3%
at474
 
1.3%
Other values (4897)29572
78.2%

Most occurring characters

ValueCountFrequency (%)
1117
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1117
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1117
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1117
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1117
100.0%

Interactions

2021-10-12T11:59:07.712990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:57:58.625738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:03.584843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:08.772501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:20.899214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:24.993654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:29.165259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:32.650573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:36.428704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:40.520307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:44.548323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:48.937460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:53.308075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:58.103133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:02.172741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:07.977224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:57:59.239452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:03.872296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:09.342782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:21.199398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:25.234215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:29.386188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:32.855948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:36.767952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:40.801484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:44.861115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:49.178675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:53.557613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:58.606001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:02.411693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:08.196782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:57:59.567691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:04.207012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:09.626338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:21.445700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:25.525343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:29.621491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:33.068480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:37.039429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:41.084646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:45.156896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:49.440339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:53.885025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:58.833678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:02.651395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:08.474977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:57:59.908153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:04.487523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:09.908683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:21.785232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:25.820914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:29.874832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:33.375470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:37.342842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:41.334605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:45.387276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:49.679599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:54.317217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:59.051284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:02.900651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:08.856415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:00.191750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:04.873292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:18.214499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:22.055959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:26.039673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:30.111640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:33.661076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:37.623348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:41.578413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:45.789521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:49.969963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:54.596227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:59.264498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:04.869014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:09.145299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:00.493992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:05.201868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:18.471269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:22.282096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:26.263978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:30.343664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:33.979273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:37.943027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:41.878204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:46.090339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:50.308088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:54.874710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:59.572843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:05.186236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:09.361345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:00.841054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:05.498192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:18.751635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:22.560400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:26.547207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:30.571792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:34.223004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:38.176489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:42.079423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:46.276139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:50.602380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:55.091149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:59.831581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:05.460775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:09.597512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:01.090008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:05.793338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:18.976170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:22.761349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:26.806491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:30.777789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:34.403464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:38.403507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:42.338822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:46.487728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:51.206153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:55.341481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:00.149343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:05.690429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:09.876440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:01.434436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:06.005356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:19.159692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:22.955878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:27.106089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:30.997070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:34.615465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:38.667086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:42.574648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:46.783084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:51.443269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:55.546478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:00.368123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:05.908087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:10.106439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:01.722951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:06.436664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:19.406507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:23.254648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:27.421257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:31.247127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:34.872318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:38.932652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:42.850513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:47.049239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:51.721453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:55.854821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:00.591236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:06.192710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:10.365438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:02.121278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:07.258586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:19.641773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:23.548462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:27.842341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:31.455148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:35.145229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:39.212912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:43.204825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:47.518529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:52.030629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:56.088376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:00.812236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:06.459309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:10.613036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:02.461677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:07.576152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:19.881065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:23.844842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:28.050475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:31.653152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:35.416711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:39.454047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:43.493732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:47.805420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:52.284314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:56.306736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:01.015165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:06.660791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:10.923517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:02.740315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:07.887671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:20.112972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:24.139418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:28.313304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:31.909144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:35.650572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:39.740996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:43.713011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:48.021423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:52.522040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:56.678839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:01.306004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:06.910851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:11.238935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:03.017842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:08.157675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:20.392167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:24.456095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:28.568554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:32.162940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:35.941791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:40.016156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:44.098601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:48.293779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:52.770392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:57.186767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:01.578207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:07.206355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:11.530010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:03.318242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:08.508668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:20.599966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:24.751246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:28.797555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:32.431845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:36.165845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:40.283141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:44.349695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:48.546533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:52.986814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:58:57.672310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:01.869438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-12T11:59:07.469977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-12T11:59:53.565005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-12T11:59:54.130945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-12T11:59:54.649793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-12T11:59:55.330462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-12T11:59:12.941546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-12T11:59:16.712107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-12T11:59:20.944643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Incident #Data SourceCountryState/Province/RegionIncident TypeIncident Date TimeIncident YearIncident MonthIncident DayTimeCityCountyEcoRegion DomainPopulation DensityTerrainSubject CategorySubject Sub-CategorySubject ActivityScenarioGroup Type# LostAgeSexWeight (Kg)Height (Cm)Physical FitnessMental FitnessExperienceClothingPersonalitySubject StatusNotify hoursSearch hoursTotal Time LostTTL HoursIPP TypeIPP ClassificationIPP Coord.IPPAccuracy (remove)Destination Coord. (remove)Temp/HTemp LWind (kph) (remove)WeatherSnow (remove)Rain (remove)Light (remove)Investigative Find (remove)Inv Find Details (remove)Suspended (remove)Subject Found featureFound Secondary (remove)Mobility Responsiveness (remove)Lost StrategyDistance IPP (km)Distance Destination (km) (remove)Distance IPP (miles) (remove)Track Offset (m)Find CoordFind AccuracyDisperson Angle (remove)Mission Close (remove)Rescue MethodResources UsedTotal Air HoursTotal PersonnelTotal Man HoursTotal CostComments
01PLIUSVANaN7/28/20042004.07.028.0NaNChesapeakeVATemperateNaNNaNDemenitaNaNNaNNaNM1.0NaNMNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.0145833330.0354166670.035416666NaNNaNNaNNaNNaNNaNNaNNaNClearNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.396241NaN0.246212NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12AUAUNaNRecovery9/30/20002000.09.030.0NaNPort BroughtonSADryNaNNaNDementiaNaNCanoeingTraumaM1.071.0M60.0178.0PoorModeratePoorFairNaNDOANaNNaNNaN0WATERNaNNaNNaNNaNNaN15.031ClearNoNoNaNNoNaNNoWaterMangrovesImmobileNaNNaNNaNNaNNaNNaNNaNNaNFoundNaNNaNNaNNaNNaNNaN71 year old male suffering Alzheimers, canoeing in bay. Deceased; found by helicopter.
23AUAUNaNSearch1/7/20002000.01.07.0NaNJondaryanQLDTemperateRuralFlatDementiaNaNDrivingLostM1.080.0M70.0170.0FairModerateGoodFairNaNInjuredNaNNaN0.2708333330.270833333BUILDINGNaNNaNNaNNaN3530.012ClearNoNoNaNNoNaNNoRoadNaNMobileTravel AidNaNNaNNaNNaNNaNNaNNaNFoundNaNNaNNaNNaNNaNNaNLeft at 0630 to drive 14km to Jondaryan. Drove through. Ran out of fuel 260 km later, still believed he was heading for Jondaryan. Located 4km E. of Wallumbilla.
34AUAUNaNSearch11/15/20012001.011.015.0NaNBallaratVICTemperateNaNFlatDementiaNaNWalkawayLostM1.070.0M50.0175.0PoorMildFairFairWithdrawnWellNaNNaN0.2638888750.263888875RESIDENCENaNVicRoads 254 D11NaNNaNNaNNaNNaNClearNoNoNaNNoNaNNoRoadNaNMobileNaN6.000000NaN3.720000NaNNaNNaNNaNFoundNaNNaNNaNNaN23.0NaNDescp broadcast to taxis, trucks, etc. MP located by taxi driver.
45AUAUNaNSearch10/9/20002000.010.09.0NaNBallaratVICTemperateUrbanFlatDementiaNaNWalkawayLostM1.088.0M60.0160.0PoorModeratePoorFairWithdrawnInjuredNaNNaN0.8618054170.861805417RESIDENCENaNMap 16 F3NaNNaN1510.0NaNOvercastNaNNaNNaNNoNaNNoWaterNaNImmobileTravel Aid1.000000NaN0.620000NaNMap 16 G5NaNNaNFoundNaNNaNNaNNaN15.0NaN88 year old male suffering dementia, wandered from Geriatric Centre. Suffering mild hyperthermia when found. Vic Roads 256 J21 to 257 L22, approx. 1000m.
56AUAUNaNSearch12/26/20012001.012.026.0NaNWangarattaVICDryUrbanFlatDementiaNaNWalkawayLostM1.068.0M78.0170.0PoorModeratePoorFairWithdrawnInjuredNaNNaN0.423611250.42361125RESIDENCENaN8125 04370 59757NaNNaN188.010ShowersNaNYesNaNNoNaNNoRoadNaNMobileStayed Put1.600000NaN0.992000NaN8125 04365 59742NaNNaNFoundNaNNaNNaNNaNNaNNaNAlzheimers patient wandered from hospital, diabetic, partially blind. Found by Mobile Police Patrol. [Moved local coord to LKP coord.]
67AUAUNaNSearch9/18/20012001.09.018.0NaNPooraka, AdelaideSADryUrbanNaNDementiaParkinsonsWalkawayInvestigativeM1.057.0MNaNNaNNaNMildNaNNaNNaNWellNaNNaN11RESIDENCENaNNaNNaNNaN20NaN0ClearNoNoNaNYesNaNNoStructureBuildingNaNNaNNaNNaNNaNNaNNaNNaNNaNFoundNaNNaNNaNNaNNaNNaNFound in hospital - resources still activated; due to misinformation re location of missing person. [Set dist=0.05]
78AUAUNaNSearch2/27/20012001.02.027.0NaNParadise PointQLDTemperateUrbanFlatDementiaNaNWalkawayLostF1.089.0F70.0160.0FairModerateGoodFairUnsureWellNaNNaN0.3958333330.395833333RESIDENCENaNNaNNaNNaN3025.0NaNClearNoNoNaNNoNaNNoRoadBus shelterMobileNaN20.000000NaN12.400000NaNNaNNaNNaNFoundNaNNaNNaNNaN9.0NaNPerson suffering dementia, wandered away from home, found in bus shelter. Had caught a bus. [Presumably found by car patrol.]
89AUAUNaNSearch9/30/20012001.09.030.0NaNKadinaSATemperateUrbanNaNDementiaNaNWalkawayLostM1.071.0MNaNNaNNaNModerateNaNNaNNaNWellNaNNaN0.0833333330.083333333RESIDENCENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFoundNaNNaNNaNNaNNaNNaN71 year old dementia sufferer refused medication and wandered off from nursing home.
910AUAUNaNSearch10/11/20012001.010.011.0NaNGlenelgSATemperateUrbanNaNDementiaNaNWalkawayLostM1.077.0MNaNNaNNaNModerateNaNNaNNaNWellNaNNaN0.2916666670.291666667RESIDENCENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoNaNNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFoundNaNNaNNaNNaNNaNNaN77 year old wandered away from nursing home and walked to old home address. [Likely missing after 8am. Then: [3 < t < 11; call it 7]

Last rows

Incident #Data SourceCountryState/Province/RegionIncident TypeIncident Date TimeIncident YearIncident MonthIncident DayTimeCityCountyEcoRegion DomainPopulation DensityTerrainSubject CategorySubject Sub-CategorySubject ActivityScenarioGroup Type# LostAgeSexWeight (Kg)Height (Cm)Physical FitnessMental FitnessExperienceClothingPersonalitySubject StatusNotify hoursSearch hoursTotal Time LostTTL HoursIPP TypeIPP ClassificationIPP Coord.IPPAccuracy (remove)Destination Coord. (remove)Temp/HTemp LWind (kph) (remove)WeatherSnow (remove)Rain (remove)Light (remove)Investigative Find (remove)Inv Find Details (remove)Suspended (remove)Subject Found featureFound Secondary (remove)Mobility Responsiveness (remove)Lost StrategyDistance IPP (km)Distance Destination (km) (remove)Distance IPP (miles) (remove)Track Offset (m)Find CoordFind AccuracyDisperson Angle (remove)Mission Close (remove)Rescue MethodResources UsedTotal Air HoursTotal PersonnelTotal Man HoursTotal CostComments
32653267PLIUSVANaN5/25/20042004.05.025.0NaNChesapeakeVATemperateNaNNaNDementialPDNaNNaNM1.052.0MNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.0097222220.0305555560.030555555NaNNaNNaNNaNNaN33.33333333NaNNaNClearNaNNaNNaNNaNNaNNaNRoadNaNNaNNaN1.676403NaN1.041667NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32663268PLIUSVANaN5/31/20042004.05.031.0NaNChesapeakeVATemperateNaNNaNDementialPDNaNNaNM1.052.0MNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.250.2708333330.270833333NaNNaNNaNNaNNaN29.44444444NaNNaNClearNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.874777NaN8.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32673269PLIUSVANaN6/10/20042004.06.010.0NaNChesapeakeVATemperateNaNNaNDementialPDNaNNaNM1.052.0MNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.0034722220.0243055560.024305555NaNNaNNaNNaNNaN32.77777778NaNNaNClearNaNNaNNaNNaNNaNNaNBusiness/developed areaNaNNaNNaN1.219202NaN0.757576NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32683270PLIUSVANaN6/11/20042004.06.011.0NaNChesapeakeVATemperateNaNNaNDementialPDNaNNaNM1.052.0MNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.0069444440.0277777780.027777777NaNNaNNaNNaNNaN25NaNNaNRainNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN0.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32693271PLIUSVANaN6/19/20042004.06.019.0NaNChesapeakeVATemperateNaNNaNDementialPDNaNNaNM1.052.0MNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.1354166670.156250.15625NaNNaNNaNNaNNaN31.11111111NaNNaNRainNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.962408NaN2.462121NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32703272PLIUSVANaN7/16/20042004.07.016.0NaNChesapeakeVATemperateNaNNaNDementialPDNaNNaNM1.052.0MNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.0006944440.0215277780.021527777NaNNaNNaNNaNNaN31.66666667NaNNaNClearNaNNaNNaNNaNNaNNaNRoadNaNNaNNaN4.828041NaN3.0000001.524019NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32713273USUSVANaN7/27/20042004.07.027.0NaNNorfolkVATemperateNaNNaNDementialPDNaNNaNM1.052.0MNaNNaNNaNNaNNaNNaNNaNWell0.0208333330.0131944440.0340277780.034027777NaNNaNNaNNaNNaN31.66666667NaNNaNCloudNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.396241NaN0.246212NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32723274NZNZNaNSearch2/14/20042004.02.014.0NaNWestern Bay of PlentyBay of PlentyTemperateWildernessWaterDementiaNaNWalkawayLostF1.070.0FNaNNaNPoorModeratePoorFairNaNWell0.031250.1041666670.1354166670.135416667NaNNaN953/774NaNNaN24NaN0ClearNaNNoNaNNaNNaNNaNLinearNaNMobileTravel Aid0.500000NaN0.310000500.000000954/775NaNNaNNaNAssist/own powerNaN1.02.014.002045.0Lost party arrived in area with two others with the intention of walking up the \n\ntrack to the Kaite Falls. Due to the steepness of the track the other two in \n\nthe party returned to carpark. Lost party continues.\n\nLost party following track. Female losses balance and falls down bank dragging \n\nhusband with her. Not able to climb back up onto track due to steepness.\n\nLost party followed river for a short period. Realised that they were lost and \n\nremained in that position until located.\n\n\n\nSearch method: Tracks all check prior to the arrival of SAR squad by Police and \n\nlocals. Not located. Due to age and medical concerns of lost party urgent \n\nresponse required. Helicopter dispatched to scene. Lost party located in river \n\nbed. Nil injuries\n\n\n\nDebrief: Held after search. Nothing of interest. Area easily contained. Only \n\nconcern is that any tracks were destroyed by locals searching in the first \n\ninstant. This cannot be avoided and must expect that this will happen given \n\ncircumstances.
32733275NZNZNaNSearch6/2/20062006.06.02.0NaNWhakataneBay of PlentyTemperateRuralMountainousDementiaNaNWalkawayLostF1.074.0FNaNNaNPoorModeratePoorFairNaNDOA3.8750.8541666674.7291666674.729166667NaNNaNNaNNaNNaN4NaNNaNRainNaNHeavyNaNNaNNaNNaNNaNMediumNaNNaN0.400000NaN0.248000NaNNaNNaNNaNNaNCarry out by footNaN1.574.0265.5010925.0Both of these people suffered from Altimers and had been getting lost and \n\nbreaking down in there car for the past week, They had fone on a familar road \n\nto Waihau Bay to get petrol and became lost on the way home and drove obnto a \n\nfarm where they got stuck there vehicle hs then run out of petrol and it \n\nappears that tyhe male has gone for help in the bad weather and come top grief \n\nwith the cold and wet and laid down under a log by the river and died fgrom the \n\ncold. The women has come to the same fate on the farm surcomming to the cold
32743276NZNZNaNSearch2/19/20062006.02.019.0NaNWellingtonWellingtonTemperateUrbanHillyDementiaNaNWalkawayLostM1.075.0MNaNNaNGoodModeratePoorFairNaNWell0.1173611110.0284722220.1458333330.145833333NaNNaNNaNNaNNaN16NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMobileWandered3.000000NaN1.860000NaNNaNNaNNaNNaNAssist/own power~Vehicle evacuationNaN0.012.016.75210.0NaN